System For Genetic Surveillance and Analysis

ABSTRACT

A genetic surveillance system comprises a communications network and at least one reader-analyzer instrument. The reader-analyzer instrument has a communication interface to communicate over the network. The reader-analyzer instrument is adapted to perform genetic assay analysis of a sample obtained from a member of a population and to generate detection-related data based upon the analysis. The reader-analyzer instrument is adapted to associate qualifying information with the detection-related data and to communicate the associated qualifying information and detection-related data over the network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/674,750, filed Apr. 26, 2005; U.S. Provisional Application No.60/699,950, filed Jul. 7, 2005; U.S. Provisional Application No.60/749,003, filed Dec. 9, 2005; U.S. Provisional Application No.60/674,876, filed Apr. 26, 2005; and U.S. Provisional Application No.60/696,157, filed Jun. 30, 2005. The disclosures of the aboveapplications are incorporated herein by reference.

All literature and similar materials cited in this application,including, but not limited to, patents, patent applications, articles,books, treatises, and internet web pages, regardless of the format ofsuch literature and similar materials, are expressly incorporated byreference in their entirety for any purpose. In the event that one ormore of the incorporated literature and similar materials differs fromor contradicts this application, including but not limited to definedterms, term usage, described techniques, or the like, this applicationcontrols.

INTRODUCTION

Currently, improved emergency preparedness and response to bioterrorism,pathogenic epidemics, and other such public health emergencies havebecome of great concern to governments, public health organizations, andthe public at large. Governments, public health institutions, and othersuch laboratories are in need of tools to aid in building networks fordetermining threats to the public. Such entities are also in need ofrapid, automated and bidirectional communications and analysis methodsto identify threats and their spatial and temporal patterns for timelyefficient response and preventative measures.

DRAWINGS

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. The drawings are not intended tolimit the scope of the present teachings in any way.

FIG. 1 is a high level system diagram illustrating a geneticsurveillance and analysis system working in conjunction with alaboratory response network;

FIG. 2 is a data flow diagram useful in understanding some of theprinciples of the genetic surveillance and analysis system;

FIG. 3 is a high level process flow diagram illustrating components ofan a genetic surveillance and analysis system;

FIG. 4 is an information system that employs a plurality ofreader-analyzer instruments;

FIG. 5 is a more detailed view further illustrating how an EpiMonitorsoftware platform may be distributed;

FIG. 6 illustrates one possible parallel processing timing diagramimplementation for sample preparation;

FIG. 7 illustrates another possible parallel processing timing diagramimplementation for sample preparation;

FIG. 8 illustrates an example of an integrated sample and assaypreparation device;

FIG. 9 is an exploded view of an assay card assembly;

FIG. 10 is an exploded view of a thermal transfer plate;

FIG. 11 is a top view of the assay card assembly of FIG. 9;

FIG. 12 is a perspective view of the thermal transfer plate of FIG. 10;

FIG. 13 is a perspective view of a 384-well assay card;

FIG. 14 illustrates a multiple cartridge portable medical device;

FIG. 15 is a perspective view of the cartridge usable by the portablemedical device of FIG. 14;

FIG. 16 is a perspective view of a handheld reader-analyzer instrument;

FIG. 17 is an exploded view of a handheld reader-analyzer instrument;

FIG. 18 illustrates an activation of a handheld reader-analyzerinstrument for an analysis;

FIG. 19 is a perspective view of the inside of an activation slider of ahandheld reader-analyzer instrument;

FIG. 20 is a flowchart illustrating a triple delta calculation;

FIG. 21 is a flowchart illustrating a triple delta calculation using PCRand microarray generated data sets;

FIG. 22 is a graphical depiction of an example of a plurality of classesof information acquired and transmitted to the EpiMonitor platform;

FIG. 23 illustrates a data structure and interaction of an exemplarydatabase;

FIG. 24 is a functional flow diagram of an exemplary communicationstrategy between an EpiMonitor server and an EpiMonitor client; and

FIG. 25 is a functional block diagram of EpiMonitor client andEpiMonitor server components for a genetic surveillance and analysissystem.

DESCRIPTION OF VARIOUS EMBODIMENTS

While the present teachings are described in conjunction with variousembodiments, it is not intended that the present teachings be limited tosuch embodiments. On the contrary, the present teachings encompassvarious alternatives, modifications, and equivalents, as will beappreciated by those of skill in the art. In the event that one or moreof the incorporated references differs from or contradicts thisapplication, including, but not limited to, defined terms, term usage,described techniques, or the like, this application controls.

In various embodiments, a genetic surveillance and analysis system 10shown in FIG. 1 illustrates the system 10 working in conjunction with alaboratory response network 50. The laboratory response network 50 hasbeen illustrated as a three-tiered, pyramidal, hierarchical arrangementof laboratories. However, the genetic surveillance and analysis system10 is not limited to operating only with networks configured in thisfashion. The hierarchical pyramid relationship is illustrated to providea better understanding of how a genetic surveillance and analysis system10, according to the present teachings, might be integrated with acontemporary laboratory response network 50, such as that promulgated bythe U.S. Department of Health and Human Services' (HHS) Centers forDisease Control and Prevention (CDC).

For example, in 1999, the CDC established a Laboratory Response Network(LRN) 50. The LRN's purpose is to coordinate a network of laboratoriesthat can respond to biological and chemical terrorism. The LRN 50 hasgrown since it was first established, and now includes a wide variety ofdifferent types of laboratories, such as state and local public healthlaboratories, veterinary laboratories, military laboratories, andinternational laboratories.

The participating laboratories are designated as either national,reference, or sentinel, depending on each individual laboratory'sfunction within the LRN 50. Sentinel labs, at the broad base of thepyramid, represent the thousands of hospital- and clinic-based labs thathave direct contact with patients. In an unannounced or covert terroristattack, specimens provided by patients during routine care mightindicate the onset of a bioterrorist attack. Similarly, specimens frompatients visiting hospitals and clinics may signal the spread of adisease. Sentinel labs are thus often the first facility to spot asuspicious specimen. The sentinel lab's responsibility is to refer thatspecimen to the proper reference lab.

Reference labs, also referred to as confirmatory reference labs, areequipped to perform tests to detect and confirm the presence of a threatagent (including those related to bioterrorism and epidemics). Theselabs ensure a timely local response in the event of a terrorist incidentor epidemic. Rather than having to rely on confirmation from nationallabs at the CDC, reference labs are capable of producing conclusiveresults upon which public health authorities are able to act. In somecases, unique abilities may be required, such as handling a highlyinfectious agent or identifying and analyzing specific agent strains.This is the function of the national laboratories, which are positionedat the narrow top of the pyramid.

The named laboratory response network 50 might suggest that anintegrated computer network joins the participating laboratories.Although computer networks and telecommunication networks, such as theInternet and the public telephone infrastructure, are utilized, there iscurrently no unified, dedicated computer network for the collection andanalysis of aggregated genetic data. The genetic surveillance andanalysis system 10 can serve this function, linking togetherparticipating members of the LRN and may also link other public health,safety, and military organizations, which may have their own separatecomputer networks.

In various embodiments, the genetic surveillance and analysis system 10may be a population security and epidemiological analysis system. Invarious embodiments, epidemiology can be the study of the distributionand determinants of disease frequency in human populations. This caninclude two main areas of investigation, one, the study of distributionand disease and two, the search for the determinants (causes of thedisease and its distribution). The first area can include describing thedistribution of health status in terms of age, gender, race, geography,time, weather conditions, and other demographics. The second area caninvolve an explanation of the patterns into the disease distribution inthe terms of causal factors.

Epidemiology can include the search for concordance between known andsuspected cause of a disease, and known patterns of distribution ofdisease, or use of these patterns to postulate elements of theenvironment that should be investigated for possible causal roles. Anexcessive frequency, or even the mere occurrence of biologicalcontaminants in environmental or biological samples, may be a feature ofmany infectious and non-infectious diseases, as well as diseases knownto be associated with microorganisms or pathogens. Identifying thefrequency of a particular disease as being excessive may be developed byfollowing its frequency over time, by comparing its frequency indifferent places, or by comparing its frequencies among subgroups in asingle population at a particular time. Such identification may includeidentifying the excessive frequency that comes about in a short periodof time and in a narrowly defined geographic area. Other terms that mayrelate to excessive frequency include epidemic, pandemic, incidence, andprevalence. In addition to frequency, mere occurrence of biologicalcontaminants in environmental or biological samples may also be ofconcern.

The genetic surveillance and analysis system 10 can utilize geneticassay technology capable of detecting and analyzing a variety ofdifferent strains of bacteria, viruses, and pathogens. As will be morefully explained below, genetic assay technology can be deployed using anassortment of different types of reader-analyzer instruments 176 thatcan be adapted for bidirectional communication through an integratingsoftware platform named EpiMonitor.

The system 10 supports a plurality of reader-analyzer instruments 176 ofdifferent sizes and capabilities, including those ranging fromsophisticated laboratory instruments 52 to portable multi-cartridgeunits (a portable instrument 54) to small, shirt-pocket-sized units suchas handheld instruments 56-1, 56-2, and 56-3. The reader-analyzerinstruments 176 analyze samples, whether taken from a patient or fromthe environment, and have varying processing ability to process theresults. In various embodiments, at least some of the reader-analyzerinstruments 176 are capable of peer-to-peer (P2P) interaction with oneanother, as illustrated diagrammatically at 58 between handheldinstruments 56-2 and 56-3. In various embodiments, at the base of thepyramid, sentinel laboratories may employ primarily handheld 56 andportable instruments 54 and, as such, these instruments may be presentin numbers on the order of thousands to tens of thousands to accommodatethe large number of sentinel laboratories. In various embodiments, thenext layer of the pyramid, reference laboratories, may employ portableinstruments 54 and more powerful laboratory instruments 52 and, as such,hundreds or thousands of these instruments may be present. In variousembodiments, national laboratories may employ hundreds of laboratoryinstruments 52.

In various embodiments, the laboratory instrument 52 can be implementedusing any one of a variety of different reader-analyzer instruments 176,such as genetic assay analysis platforms. Suitable platforms include themodel 7500 fast real-time PCR system and the 7900 HT fast real-time PCRsystem, both available from Applied Biosystems, Foster City, Calif.Other genetic assay analysis platforms can also be used such as, forexample, PCR instruments commercially available from Bio Rad,Strategene, Roche Applied Science, Techne Quantica, and Cepheid, as wellas, PCR instruments that operate using isothermal methods. Still,examples of other genetic analysis platforms that may be useful hereininclude microarray technology such as those commercially available fromApplied Biosystems, Affymetrix, Agilent, Illumina, and Xeotron.Typically, the laboratory instrument 52 would be deployed, for example,in hospital laboratory, at a university, or in a government publichealth laboratory, which may or may not be a participating member of thelaboratory response network.

In various embodiments, a reader-analyzer instrument 176 can be portableinstrument 54, which can be physically smaller than the laboratoryinstrument 52 to make it suitable for deployment in a doctor's office orsmall clinic. It can be connected to a computer, eliminating the needfor on-board processing. The portable instrument 54 is generally capableof analyzing fewer samples for fewer target sequences than thelaboratory instrument 52.

In various embodiments, a reader-analyzer instrument 176 can be ahandheld instrument 56 and may represent an economical end of theinstrument spectrum. In various embodiments, the handheld instrument 56may be of convenient, portable size (e.g., approximately the size of adeck of playing cards). It can be configured to detect a specificdisease such as multidrug-resistant tuberculosis. The handheldinstrument 56 can be capable of analyzing samples obtained in a varietyof different forms including sputum samples, blood samples, and thelike. The handheld instrument 56 can be battery powered and can includean embedded internal controller so that no external computer isrequired.

Reader-analyzer instruments 176 that may be used in the system are notlimited to such instruments that can perform PCR. Any instrument thatcan provide data on the analysis of pathogens such as identifying astrain of bacteria, fungi, virus, and the like, may be integrated intothe genetic surveillance and analysis system 10. Examples of other suchreader-analyzer instruments 176 include mass spectrometers, which mayinclude the use of MADLI, chromatography, pyrolization, and other suchtechniques for introducing a sample, DNA micro arrays such as, forexample, those commercially available from Affymetrix, Agilent,Illumina, Xeotron, and Applied Biosystems, as well as those systems thatmay be developed in-house by a particular laboratory, and may alsoinclude instruments capable of detection using an antibody such asELISA, and the like.

While the instruments described above are adapted for processing asample obtained from a human, plant or animal, the genetic surveillanceand analysis system 10 can be readily adapted to utilize other types ofinput devices, such as environmental sensors. Environmental sensors suchas, for example, air samplers 60, water samplers 62 for bodies of water(e.g., reservoirs, tanks, lakes, etc.), as well as other samplingconfigurations, can be readily adapted for use with the presentteachings. The environmental samplers 60, 62 can be adapted to analyzesamples taken from strategic locations. The results obtained byanalyzing those samples can be integrated with the data being collectedby reader-analyzer instruments 176 via the EpiMonitor software platform100, described more fully below.

Referring now to FIG. 2, the genetic surveillance and analysis system 10utilizes an EpiMonitor software platform 100 that integrates variousreader-analyzer instruments 176 within a database system. The EpiMonitorsoftware platform 100 organizes and supports bidirectional communicationamong a collection 102 of reader-analyzer instruments 176, eachanalyzing samples of genetic data.

The collection 102 provides reaction data and contextual data to aqueue/security server 104 of the EpiMonitor software platform 100. Invarious embodiments, the queue/security server 104 can establish secureconnections with the collection 102 of reader-analyzer instruments 176,verify that data has been received uncorrupted, and queue received datafor processing. The queue/security server 104 can communicate with thecollection 102 of reader-analyzer instruments 176 through a variety ofintermediaries, including the public Internet, Virtual Private Networks,and private networks. The queue/security server 104 can also communicateinformation, such as sample preparation instructions, to the collection102 of reader-analyzer instruments 176.

The queue/security server 104 provides data to an observation/analyticalserver 106 of the EpiMonitor software platform 100. In variousembodiments, the observation/analytical server 106 can pre-process data,perform rules-based analysis, and discern data trends. Together, theservers 104 and 106 validate, collect, and analyze data, as described inmore detail below. The servers 104 and 106 can be implemented asstand-alone servers, as a single unified server, or as a distributedsystem of multiple servers. In addition, various functions of theservers 104 and 106 can be distributed to the collection 102 ofreader-analyzer instruments 176 or to computers associated with any ofthe collection 102 of reader-analyzer instruments 176.

The collection 102 of reader-analyzer instruments 176 depicted includesa portable instrument 54, a laboratory instrument 52, a handheldinstrument 56-1, a handheld instrument 56-4, which communicates with thequeue/security server 104 via a stand-alone computer 114, andenvironmental samplers 60, 62. The observation/analytical server 106 canstore reaction and contextual data obtained from the collection 102 ofreader-analyzer instruments 176 into a suitable database. Theobservation/analytical server 106 can provide access to this databasevia an HTML (hypertext markup language) web interface to a remote client120-1. A web browser within the remote client 120-1 can displayobservations and analysis from the observation/analytical server 106.Examples of web browser displays include “Biomarker: ABC, Incidenceduring January 2003-December 2004: XX, Prevalence during January2003-December 2004: XY,” and “Date/Time: 11/19/2004, 8:00 AM, Region:Northern California, Biomarker: ABC, Current Incidence: XX, CurrentPrevalence: XY.” Access to the database can also be providedprogrammatically via web services, such as to a second remote client120-2.

The observation/analytical server 106 can integrate data from sourcesother than the collection 102 of reader-analyzer instruments 176. Toconvert external data sources into a standard form that theobservation/analytical server 106 can process, the EpiMonitor softwareplatform 100 includes, in various embodiments, a data integration server108. The data integration server 108 communicates with contextual datastores 122. Contextual data stores 122 can include medical records, suchas an electronic medical record server 124 located at hospital A.Contextual data stores 122 can also include national retail information126, a demographic/census data store 128, such as provided by the U.S.Census Bureau, and a data store 130 of the CDC Public Health InformationNetwork.

FIG. 3 illustrates a functional flow diagram for a simple geneticsurveillance and analysis system 10 implementation. One or more samples170 are taken from human, plant or animal subjects (or from othersources such as environmental sampler units). The samples 170 areprocessed, for example, using a stand-alone Integrated Sample and AssayPreparation (ISAP) module 172. In this regard, the user provides thesample 170 as an input to the ISAP module 172, and the ISAP module 172provides a standardized card, such as a microfluidic card 174, as itsoutput. If desired, the ISAP module 172 can be integrated with theEpiMonitor software platform 100 and thus provide communicationcapability. Such communication capability and integration into theEpiMonitor software platform 100 allows the operating parameters of theISAP module 172 to be updated or controlled remotely to conform itsoperation to rapidly changing analysis parameters and assays.

Once the microfluidic card 174 has been filled with properly preparedsample 170 and PCR reagents including at least one primer probe set, thecard 174 can be then inserted into a reader-analyzer instrument 176. Insome embodiments, reader-analyzer instrument 176 can be a geneticanalysis platform such as, for example, a PCR system. In variousembodiments, reader-analyzer instrument 176 may be any of thoseillustrated in FIG. 1 as instruments 52, 54, and 56. In variousembodiments, the reader-analyzer instrument 176 may be capable ofperforming reverse transcription PCR (RT-PCR), further described below,either simultaneously with PCR or as a separate step.

In various embodiments, the output of the reader-analyzer instrument176, either as raw data or processed data, can be processed byEpiMonitor software 100 and information extracted from this analysis canbe stored in a suitable database 180. The database 180 can be at acentral location or it can be distributed across multiple locations. Invarious embodiments, the EpiMonitor software 100 can mediatebidirectional communication between the components that make up thesystem such as, for example, the reader-analyzer instrument 176 anddatabase 180, in some implementations also the ISAP module 172 andmicrofluidic card 174). Although a single data flow has been illustrated(from sample 170 to database 180), similar data flows can occurconcurrently at multiple locations distributed throughout the world. TheEpiMonitor software 100 coordinates this data gathering among apotentially large number of instruments 176 and ensures that theinformation extracted from a plurality of reader-analyzer instruments176 can be stored in the database 180 in a consistent manner thatfacilitates further operations on the collected information such as, forexample, statistical analysis.

FIG. 4 illustrates a flowchart of a parallel workflow implementation. Inthe workflow illustrated in FIG. 3, a single ISAP module 172 suppliedmicrofluidic cards 174 serially to one reader-analyzer instrument 176.In FIG. 4, a single ISAP module 172 prepares a plurality of microfluidiccards 174 for a plurality of reader-analyzer instruments 176-1, 176-2,and 176-3, operating in parallel. This arrangement may be most usefulwhen an analysis uses a short sample and assay preparation time by theISAP module 172 to prepare a microfluidic card 174 as compared to thetime required for analyzing the microfluidic card 174 by one of theplurality of reader-analyzer instrument 176. As such, a single ISAPmodule 172 can supply a plurality of reader-analyzer instruments 176without delays.

Referring to FIG. 5, each of the individual devices and instruments(ISAP module 172, microfluidic card 174, reader-analyzer instruments176-1 and 176-2, and database 180) can be embedded or associatedcomponents of the EpiMonitor software platform 100. The reader-analyzerinstruments 176-1 and 176-2 can establish a peer-to-peer relationship,where they communicate information between one another or through anassociated network. The interconnecting lines (without arrowheads) ofFIG. 5 represent communication pathways for data and/or controlinstructions made possible by the EpiMonitor software platform 100. Thedirected arrows show relationships among the interconnected components.Thus, as illustrated, the ISAP module 172 produces the microfluidic card174. In various embodiments, the microfluidic card 174 can be analyzedby reader-analyzer 176-1, which communicates peer-to-peer withreader-analyzer 176-2. The reader-analyzer 176-2 reports to the database180. The relationships among components, and the functions that theyperform, are managed by the EpiMonitor software platform 100. TheEpiMonitor software platform 100 can include components associated withthe LRN 50, or with other networks 190, to coordinate analysis andresponse capabilities.

FIGS. 6 and 7 each illustrate a possible parallel processing timingdiagram. Referring to FIG. 6, the ISAP module 172 produces a firstmicrofluidic card 174, which is then processed by the firstreader-analyzer instrument 176-1. While processing is taking placewithin the first instrument 176-1, the ISAP module 172 prepares a secondmicrofluidic card 174 so that it will be ready for processing by theinstrument 176-1 once the first assay processing is completed. In asecond parallel processing arrangement, illustrated in FIG. 7, the ISAPmodule 172 successively prepares three different assays (assay 1, assay2, assay 3), which are individually processed by three differentreader-analyzer instruments 176-1, 176-2, and 176-3.

In each of the reader-analyzer instruments 176 described above, thesample can be tested against a specific assay panel. In variousembodiments, such a panel can include desired reagents (e.g., enzymes,primers, probes, etc., when using PCR as discussed below) that are usedto perform an assay for target sequences of interest. In an exemplaryfull-featured system application, the reagents can include a compoundset for detecting a plurality of selected bacterial spores,gram-positive or gram-negative bacteria, and/or viruses (whether DNA orRNA-based). A combination of these target sequences can define an assaypanel particularly useful for a particular diagnosis.

In various embodiments, one example of such an assay panel can be anupper respiratory panel that includes several of the most commonbacterial and viral pathogens responsible for, or associated with, upperrespiratory infectious disease as shown in Table 1. The exemplary assaypanel includes twenty-one distinct pathogens and five controls (GAPDH,IPC1, IPC10, IPC0.1, and buffer). In this example, six of the twenty-onepathogens are included a second time with the incorporation of aninternal positive control (IPC).

TABLE 1 Assay Name Pathogen B-pert/holm/1000x Bordetella pertussis +1000x IPC C-pneu/100x Chlamydia pneumoniae + 100x IPC L-pneu/10xLegionella pneumophila + 10x IPC M-pneu/1x Mycoplasma pneumoniae + 1xIPC C-botu/0.1x Clostridium botulinum + 0.1x IPC S-pneu/0.01xStreptococcus pneumoniae + 0.01x IPC M-cata Moraxella catarrhalis N-meniNeisseria meningitidis H-infl Haemophilus influenzae (Type B) S-aureStapphylococcus Aureus GAPDH GAPDH control (for QC purposes) M-tubeMycobacterium tuberculosis RSV-A Respiratory Syncytial Virus (RSV) ARSV-B Respiratory Syncytial Virus (RSV) B Hadv-1-2-5-6 Human AdV (Types1, 2, 5, 6) EntV Enterovirus SARS3-CoV SARS-CoV Metapneu-VMetapneumovirus Infl-A Influenza A Infl-B Influenza B CMVCytomegalovirus PIV Parainfluenza Virus B-pert/holm Bordetella pertussisC-pneu Chlamydia pneumoniae L-pneu Legionella pneumophila M-pneuMycoplasma pneumoniae C-botu Clostridium Botulinum S-pneu Streptococcuspneumoniae IPC1 Internal Positive control IPC10 Internal Positivecontrol IPC0.1 Internal Positive Control Buffer Negative Control

Multiple configurations of such assay panels can be created and otherpanels can be configured as desired. The number of target sequences inthe panel may depend on factors, including the nature of the panel andthe implementation of a specific instrumentation platform ofreader-analyzer 176. In one exemplary application, a laboratoryinstrument 52 might perform between approximately 10-20 assays while ahandheld instrument 56 may perform fewer, possibly just one assay. Inaddition to an assay panel that includes multiple pathogenic species,the assay panel can include multiple strains of a particular pathogenfor purposes such as identifying potential drug resistances, therebyproviding a potential guide to effective therapy. The assay panel canalso contain multiple DNA targets or other target sequences for a singlepathogen to potentially improve specificity in detection. Otherpotential assay panel combinations/formulations can be devised fornumerous useful purposes. For example, an assay panel could includeavian flu H5:N1 or a group of strains of pathogenic E. Coli which couldinclude 0157:H7.

In various embodiments, to create an assay panel, an ISAP module 172,such as that illustrated in FIG. 8, can be used. The ISAP module 172 canaccept one or more microfluidic cards 174 into a loading tray 214. A lid216 of the ISAP module 172 can be raised so that reagent holding devices218 can be removed and inserted. These reagent holding devices 218provide the ISAP module 172 with a supply of reagents that areselectively introduced to the microfluidic card 174.

The ISAP module 172 can accept samples 170 from the environment or froma patient, such as nasal, throat, and/or nasopharyngeal swabs. The ISAPmodule 172 treats samples of liquid expressed from these swabs tofacilitate lysis and ready the sample for purification. The nucleicacids produced are highly pure and free of cross-contamination.Purification reagents can also be added manually to the microfluidiccard 174 by the ISAP module 172 operator. An optional graphical userinterface incorporated to the ISAP module 172 can provide easy access topre-programmed methods, and affords the ability to create, edit, andstore custom purification routines. It should also be appreciated thatthe present teachings may be used in connection with microfluidic cards174 and other principles, such as set forth in U.S. Pat. Nos. 6,124,138and 6,126,899.

Table 2 depicts an exemplary layout of a microfluidic card 174, giventhe pathogen panel of Table 1. There are sixteen rows (A-P), andtwenty-four columns (1-24), yielding 384 (16*24=384) wells. Because eachtarget sequence/control can be repeated at least eight times in the cardlayout, this card can simultaneously process eight samples.

TABLE 2 1 2 3 4 5 6 7 8 9 10 A B-pert/holm/1000x C-pneu/100x L-pneu/10xM-pneu/1x C-botu/0.1x S-pneu/0.01x M-cata N-meni H-infl S-aure BB-pert/holm C-pneu L-pneu M-pneu C-botu S-pneu M-cata N-meni H-inflS-aure C B-pert/holm/1000x C-pneu/100x L-pneu/10x M-pneu/1x C-botu/0.1xS-pneu/0.01x M-cata N-meni H-infl S-aure D B-pert/holm C-pneu L-pneuM-pneu C-botu S-pneu M-cata N-meni H-infl S-aure E B-pert/holm/1000xC-pneu/100x L-pneu/10x M-pneu/1x C-botu/0.1x S-pneu/0.01x M-cata N-meniH-infl S-aure F B-pert/holm C-pneu L-pneu M-pneu C-botu S-pneu M-cataN-meni H-infl S-aure G B-pert/holm/1000x C-pneu/100x L-pneu/10xM-pneu/1x C-botu/0.1x S-pneu/0.01x M-cata N-meni H-infl S-aure HB-pert/holm C-pneu L-pneu M-pneu C-botu S-pneu M-cata N-meni H-inflS-aure I B-pert/holm/1000x C-pneu/100x L-pneu/10x M-pneu/1x C-botu/0.1xS-pneu/0.01x M-cata N-meni H-infl S-aure J B-pert/holm C-pneu L-pneuM-pneu C-botu S-pneu M-cata N-meni H-infl S-aure K B-pert/holm/1000xC-pneu/100x L-pneu/10x M-pneu/1x C-botu/0.1x S-pneu/0.01x M-cata N-meniH-infl S-aure L B-pert/holm C-pneu L-pneu M-pneu C-botu S-pneu M-cataN-meni H-infl S-aure M B-pert/holm/1000x C-pneu/100x L-pneu/10xM-pneu/1x C-botu/0.1x S-pneu/0.01x M-cata N-meni H-infl S-aure NB-pert/holm C-pneu L-pneu M-pneu C-botu S-pneu M-cata N-meni H-inflS-aure O B-pert/holm/1000x C-pneu/100x L-pneu/10x M-pneu/1x C-botu/0.1xS-pneu/0.01x M-cata N-meni H-infl S-aure P B-pert/holm C-pneu L-pneuM-pneu C-botu S-pneu M-cata N-meni H-infl S-aure 11 12 13 14 15 16 17 1819 20 21 22 23 24 A GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoVMetapneu-V Infl-A Infl-B CMV PIV IPC10 IPC1 B GAPDH M-tube RSV-A RSV-BHadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-A Infl-B CMV PIV IPC0.1 BL CGAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-AInfl-B CMV PIV IPC10 IPC1 D GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntVSARS3-CoV Metapneu-V Infl-A Infl-B CMV PIV IPC0.1 BL E GAPDH M-tubeRSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-A Infl-B CMV PIVIPC10 IPC1 F GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoVMetapneu-V Infl-A Infl-B CMV PIV IPC0.1 BL G GAPDH M-tube RSV-A RSV-BHadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-A Infl-B CMV PIV IPC10 IPC1H GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-AInfl-B CMV PIV IPC0.1 BL I GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntVSARS3-CoV Metapneu-V Infl-A Infl-B CMV PIV IPC10 IPC1 J GAPDH M-tubeRSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-A Infl-B CMV PIVIPC0.1 BL K GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoVMetapneu-V Infl-A Infl-B CMV PIV IPC10 IPC1 L GAPDH M-tube RSV-A RSV-BHadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-A Infl-B CMV PIV IPC0.1 BL MGAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-AInfl-B CMV PIV IPC10 IPC1 N GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntVSARS3-CoV Metapneu-V Infl-A Infl-B CMV PIV IPC0.1 BL O GAPDH M-tubeRSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoV Metapneu-V Infl-A Infl-B CMV PIVIPC10 IPC1 P GAPDH M-tube RSV-A RSV-B Hadv-1-2-5-6 EntV SARS3-CoVMetapneu-V Infl-A Infl-B CMV PIV IPC0.1 BL

In various embodiments, microfluidic card 174 may be other than the384-well microfluidic card 174 layout and filling systems and/ormicrofluidics can also be used in implementing loading samples andreagents to the microfluidic card 174. By way of non-limitingillustration, a centrifugal filling system and microfluidic card systemdescribed in U.S. Pat. No. 6,627,159 can be used to fill the card fromthe loading ports. An exemplary centrifuge can be the Sorvall® Legend TCentrifuge with a 4-Place Swinging Bucket Rotor twist-on fixture, whichdoes not require a tool to secure it to the centrifuge.

Referring now to FIG. 9, an example of a microfluidic card 174, whichmay be injection molded, serves as the base for microfluidic cardassembly 246. A gas-permeable membrane 244 can be placed onto themicrofluidic card 174. Above the membrane 244 can be situated a thinfilm cover layer 242, including vent holes 248. Referring now to FIG.10, a thermal transfer plate 260 can be demonstrated. The thermaltransfer plate 260 can be interfaced with the microfluidic card assembly246 of FIG. 9. The thermal transfer plate 260 seals inlet and outletchannels of the microfluidic card assembly 246 to isolate reactionchambers 264. Circular thermal dies 262 (which have an internal diameterof 2.5 mm in various embodiments) of the thermal transfer plate 260create a heat seal around each reaction chamber 264.

FIG. 11 illustrates a top view of the microfluidic card assembly 246.The gas permeable membrane 244 holds up liquid during filling. A venthole 248 can be located above the gas permeable membrane 244. A reactionchamber 264 and a sample inlet port 282 is shown, into which samples canbe loaded from an ISAP module 172, or manually with a syringe. Invarious embodiments, the microfluidic card 174 can be loaded with avolume of 100 μL, and each well has a volume of 1.5 μL.

FIG. 12 illustrates a perspective view of the thermal transfer plate 260of FIG. 10. Each of the 384 thermal transfer dies 262 isolates reactionchambers 264 (not shown) in the microfluidic card 174. The size of thecircular dies 262 depends upon reaction chamber 264 size, but in variousembodiments, can be about a 2.5 mm inside diameter. Referring now toFIG. 15, a perspective view of the microfluidic card 174 comprisingmicrochannels within the microfluidic card 174 can be routed to createany arbitrary layout desired. In various embodiments, the card can beabout 85.7 mm in width and about 127.0 mm in length.

In various embodiments, microfluidic card 174 themselves can be providedwith embedded processor capability. For example, the microfluidic card174 can be provided with one or more thermal sensors, thereby allowingactual thermal data to be collected by the reader-analyzer instrument176. In various embodiments, the EpiMonitor software platform 100 cansupply the microfluidic card 174 with data, which can be used in theevent that subsequent tests or quality control procedures may be needed.Such a capability can be provided by including a SmartCard, RFID, orother such semiconductor device mounted in the microfluidic card 174. Invarious embodiments, microfluidic card 174 can also communicate with therest of the system 10 using the EpiMonitor software platform 100. Invarious embodiments, a reader-analyzer instrument 176 can perform PCR onprepared assay panels, and detect resulting fluorescence. Thereader-analyzer instrument 176 can also process this data to estimatethe number of copies of a target sequence initially present in a sample,or whether a particular target sequence may be present. In variousembodiments, the reader-analyzer instrument 176 can be controlled by acomputer or laptop, so that processing power can be A connection betweenthe reader-analyzer instrument 176 and the computer can be wired orwireless, and the connection between the computer and the server hostingEpiMonitor software platform 100 may be wired or wireless. In variousembodiments, reader-analyzer instrument 176 is connected to a computerthat can be part of a client server system and, in various embodiments,at least part of the EpiMonitor software 100 host at a server may bedownloaded to the chart for numbering crunching and/or data analysis atthe client.

In various embodiments, a Fast real-time PCR option can give real-timePCR results in a 96-well format in approximately 35 minutes, inclusiveof sample preparation. In various embodiments, reader-analyzerinstruments 176 used for the amplification of polynucleic acids, such asby PCR. Briefly, by way of background, PCR can be used to amplify asample of target Deoxyribose Nucleic Acid (DNA) for analysis. Typically,the PCR reaction involves copying the strands of the target DNA and thenusing the copies to generate additional copies in subsequent cycles.Each cycle doubles the amount of the target DNA present, therebyresulting in a geometric progression in the number of copies of thetarget DNA. The temperature of a double-stranded target DNA is elevatedto denature the DNA, and the temperature is then reduced to anneal atleast one primer to each strand of the denatured target DNA. In variousembodiments, the target DNA can be a cDNA.

In various embodiments, primers are used as a pair—a forward primer anda reverse primer—and can be referred to as a primer pair or primer set.In various embodiments, the primer set comprises a 5′ upstream primerthat can bind with the 5′ end of one strand of the denatured target DNAand a 3′ downstream primer that can bind with the 3′ end of the otherstrand of the denatured target DNA. Once a given primer binds to thestrand of the denatured target DNA, the primer can be extended by theaction of a polymerase. In various embodiments, the polymerase can be athermostable DNA polymerase, for example, a Taq polymerase. The productof this extension, which sometimes may be referred to as an amplicon,can then be denatured from the resultant strands and the process can berepeated. Temperatures suitable for carrying out the reactions are wellknown in the art. Certain basic principles of PCR are set forth in U.S.Pat. Nos. 4,683,195, 4,683,202, 4,800,159, and 4,965,188, each issued toMullis et al.

In various embodiments, PCR can be conducted under conditions allowingfor quantitative and/or qualitative analysis of one or more target DNA.Accordingly, detection probes can be used for detecting the presence ofthe target DNA in an assay. In various embodiments, the detection probescan comprise physical (e.g., fluorescent) or chemical properties thatchange upon binding of the detection probe to the target DNA. Variousembodiments of the present teaching can provide real timefluorescence-based detection and analysis of amplicons as described, forexample, in PCT Publication No. WO 95/30139 and U.S. patent applicationSer. No. 08/235,411.

In various embodiments, a sample can be analyzed as a homogenouspolynucleotide amplification assay, for coupled amplification anddetection, wherein the process of amplification generates a detectablesignal and the need for subsequent sample handling and manipulation todetect the amplified product is minimized or eliminated. Homogeneousassays can provide for amplification that is detectable without openinga sealed well or further processing steps once amplification isinitiated. Such homogeneous assays can be suitable for use inconjunction with detection probes. For example, in various embodiments,the use of an oligonucleotide detection probe, specific for detecting aparticular target DNA can be included in an amplification reaction inaddition to a DNA binding agent of the present teachings.

Homogenous assays among those useful herein are described, for example,in commonly assigned U.S. Pat. No. 6,814,934. In various embodiments,methods are provided for detecting a plurality of targets. Such methodsinclude those comprising forming an initial mixture comprising ananalyte sample suspected of comprising the plurality of targets, apolymerase, and a plurality of primer sets. In various embodiments, eachprimer set comprises a forward primer and a reverse primer and at leastone detection probe unique for one of the plurality of primer sets. Invarious embodiments, the initial mixture can be formed under conditionsin which one primer elongates if hybridized to a target.

In various embodiments, reagents are provided comprising a master mixcomprising at least one of catalysts, initiators, promoters, cofactors,enzymes, salts, buffering agents, chelating agents, and combinationsthereof. In various embodiments, reagents can include water, a magnesiumcatalyst (such as MgCl2), polymerase, a buffer, and/or dNTP. In variousembodiments, specific master mixes can comprise AmpliTaq® Gold PCRMaster Mix, TaqMan® Universal Master Mix, TaqMan® Universal Master MixNo AmpErase® UNG, Assays-by-DesignSM, Pre-Developed Assay Reagents(PDAR) for gene expression, PDAR for allelic discrimination andAssays-On-Demand®, (all of which are marketed by Applied Biosystems).However, the present teachings should not be regarded as being limitedto the particular chemistries and/or detection methodologies recitedherein, but may employ Taqman®; Invader®; Taqman Gold®; protein,peptide, and immuno assays; receptor binding; enzyme detection; andother screening and analytical methodologies.

In various embodiments, a solid support such as, for example, amicroplate or a microfluidic card 174, can be covered with a sealingliquid prior to performance of analysis or reaction of assay. Forexample, in various embodiments, a sealing liquid can be applied to thesurface of a microplate comprising reaction spots comprising an assay orfor amplification of polynucleotides. In various embodiments, a sealingliquid can be a material which substantially covers the materialretention regions (e.g., reaction spots) on the microplate so as tocontain materials present in the material retention regions, andsubstantially prevent movement of material from one reaction region toanother reaction region on the substrate. In various embodiments, thesealing liquid can be any material which is not reactive with assayunder normal storage or usage conditions. In various embodiments, thesealing liquid can be substantially immiscible with assay.

In various embodiments, the sealing liquid can be transparent, have arefractive index similar to glass, have low or no fluorescence, have alow viscosity, and/or be curable. In various embodiments, the sealingliquid can comprise a flowable, curable fluid such as a curable adhesiveselected from the group consisting of: ultra-violet-curable and otherlight-curable adhesives; heat, two-part, or moisture activatedadhesives; and cyanoacrylate adhesives. In various embodiments, thesealing liquid can be selected from the group consisting of mineral oil,silicone oil, fluorinated oils, and other fluids that are substantiallynon-miscible with water. In various embodiments, the sealing liquid canbe a fluid when it is applied to the surface of the microplate and invarious embodiments, the sealing liquid can remain fluid throughout ananalytical or chemical reaction using the microplate. In variousembodiments, the sealing liquid can become a solid or semi-solid afterit is applied to the surface of the microplate.

As should be appreciated from the discussion herein, the presentteachings can find utility in a wide variety of amplification methods,such as PCR, Reverse-Transcription PCR (RT-PCR), Ligation Chain Reaction(LCR), Nucleic Acid Sequence Based Amplification (NASBA), self-sustainedsequence replication (3SR), strand displacement activation (SDA), Q(3replicase) system, isothermal amplification methods, and other knownamplification method or combinations thereof. Additionally, the presentteachings can find utility for use in a wide variety of analyticaltechniques, such as ELISA; DNA and RNA hybridizations; antibody titerdeterminations; gene expression; recombinant DNA techniques; hormone andreceptor binding analysis; and other known analytical techniques. Stillfurther, the present teachings can be used in connection with suchamplification methods and analytical techniques using not onlyspectrometric measurements, such as absorption, fluorescence,luminescence, transmission, chemiluminescence, and phosphorescence, butalso colorimetric or scintillation measurements or other known detectionmethods.

In various embodiments, the reagents can comprise first and secondoligonucleotides effective to bind selectively to adjacent, contiguousregions of target DNA and that can be ligated covalently by a ligaseenzyme or by chemical means. Such oligonucleotide ligation assays (OLA)are described, for example, in U.S. Pat. No. 4,883,750; and Landegren,U., et al., Science 241:1077 (1988). In various embodiments, a detectionprobe comprises a moiety that facilitates detection of a nucleic acidsequence, and in various embodiments, quantifiably. In variousembodiments, a detection probe can comprise, for example, a fluorophoresuch as a fluorescent dye, a hapten such as a biotin or a digoxygenin, aradioisotope, an enzyme, or an electrophoretic mobility modifier. Invarious embodiments, the level of amplification can be determined usinga fluorescently labeled oligonucleotide. In various embodiments, adetection probe can comprise a fluorophore further comprising afluorescence quencher.

In various embodiments, a detection probe can comprise a fluorophore andcan be, for example, a 5′-exonuclease assay probe such as a TaqMan®probe (marketed by Applied Biosystems), a stem-loop Molecular Beacon(see, e.g., U.S. Pat. Nos. 6,103,476 and 5,925,517, Nature Biotechnology14:303-308 (1996); Vet et al., Proc Natl Acad Sci USA. 96:6394-6399(1999)), a stemless or linear molecular beacon (see., e.g., PCT PatentPublication No. WO 99/21881), a Peptide Nucleic Acid (PNA) MolecularBeacon™ (see, e.g., U.S. Pat. Nos. 6,355,421 and 6,593,091), a linearPNA Molecular Beacon (see, e.g., Kubista et al., SPIE 4264:53-58(2001)), a flap endonuclease probe (see, e.g., U.S. Pat. No. 6,150,097),a Sunrise®/Amplifluor® probe (see, e.g., U.S. Pat. No. 6,548,250), astem-loop and duplex Scorpion™ probe (see, e.g., Solinas et al., NucleicAcids Research 29:E96 (2001), and U.S. Pat. No. 6,589,743), a bulge loopprobe (see, e.g., U.S. Pat. No. 6,590,091), a pseudo knot probe (see,e.g., U.S. Pat. No. 6,589,250), a cyclicon (see, e.g., U.S. Pat. No.6,383,752), an MGB Eclipse™ probe (Marketed by Epoch Biosciences), ahairpin probe (see, e.g., U.S. Pat. No. 6,596,490), a peptide nucleicacid (PNA) light-up probe, a self-assembled nanoparticle probe, or aferrocene-modified probe described, for example, in U.S. Pat. No.6,485,901; Mhlanga et al., Methods 25:463-471 (2001); Whitcombe et al.,Nature Biotechnology 17:804-807 (1999); Isacsson et al., Molecular CellProbes 14:321-328 (2000); Svanvik et al., Anal. Biochem. 281:26-35(2000); Wolffs et al., Biotechniques 766:769-771 (2001), Tsourkas etal., Nucleic Acids Research 30:4208-4215 (2002); Riccelli et al.,Nucleic Acids Research 30:4088-4093 (2002); Zhang et al., Sheng Wu HuaXue Yu Sheng Wu Li Xue Bao (Shanghai) (Acta Biochimica et BiophysicaSinica) 34:329-332 (2002); Maxwell et al., J. Am. Chem. Soc.124:9606-9612 (2002); Broude et al., Trends Biotechnol. 20:249-56(2002); Huang et al., Chem Res. Toxicol. 15:118-126 (2002); Yu et al.,J. Am. Chem. Soc 14:11155-11161 (2001).

In various embodiments, a detection probe can comprise a sulfonatederivative of a fluorescent dye, a phosphoramidite form of fluorescein,or a phosphoramidite forms of CY5. Detection probes among those usefulherein are also disclosed, for example, in U.S. Pat. Nos. 5,188,934,5,750,409, 5,847,162, 5,853,992, 5,936,087, 5,986,086, 6,020,481,6,008,379, 6,130,101, 6,140,500, 6,140,494, 6,191,278, and 6,221,604.Energy transfer dyes among those useful herein include those describedin U.S. Pat. Nos. 5,728,528, 5,800,996, 5,863,727, 5,945,526, 6,335,440,6,849745, U.S. Patent Application Publication No. 2004/0126763 A1, PCTPublication No. WO 00/13026A1, PCT Publication No. WO 01/19841A1, U.S.Patent Application Ser. No. 60/611,119, filed Sep. 16, 2004, and U.S.patent application Ser. No. 10/788,836, filed Feb. 26, 2004. In variousembodiments, a detection probe can comprise a fluorescence quencher suchas a black hole quencher (marketed by Metabion International AG), anIowa Black™ quencher (marketed by Integrated DNA Technologies), a QSYquencher (marketed by Molecular Probes), and Dabsyl and Eclipse™ DarkQuenchers (marketed by Epoch).

In various embodiments, amplified sequences can be detected indouble-stranded form by a detection probe comprising an intercalating ora crosslinking dye, such as ethidium bromide, acridine orange, or anoxazole derivative, for example, SYBR Green® (marketed by MolecularProbes, Inc.), which exhibits a fluorescence increase or decrease uponbinding to double-stranded nucleic acids. In various embodiments, adetection probe comprises SYBR Green® or Pico Green® (marketed byMolecular Probes, Inc.). In various embodiments, a detection probe cancomprise an enzyme that can be detected using an enzyme activity assay.An enzyme activity assay can utilize a chromogenic substrate, afluorogenic substrate, or a chemiluminescent substrate. In variousembodiments, the enzyme can be an alkaline phosphatase, and thechemiluminescent substrate can be(4-methoxyspiro[1,2-dioxetane-3,2′(5′-chloro)-tricyclo[3.3.1.13,7]decan]-4-yl)phenylphosphate.In various embodiments, a chemiluminescent alkaline phosphatasesubstrate can be CDP-Star® chemiluminescent substrate or CSPD®chemiluminescent substrate (marketed by Applied Biosystems).

In various embodiments, the present teachings provide methods andapparatus for Reverse Transcriptase PCR (RT-PCR), which include theamplification of a Ribonucleic Acid (RNA) target. In variousembodiments, assay can comprise a single-stranded RNA target, whichcomprises the sequence to be amplified (e.g., an mRNA), and can beincubated in the presence of a reverse transcriptase, two primers, a DNApolymerase, and a mixture of dNTPs suitable for DNA synthesis. Duringthis process, one of the primers anneals to the RNA target and can beextended by the action of the reverse transcriptase, yielding anRNA/cDNA doubled-stranded hybrid. This hybrid can be then denatured andthe other primer anneals to the denatured cDNA strand. Once hybridized,the primer can be extended by the action of the DNA polymerase, yieldinga double-stranded cDNA, which then serves as the double-stranded targetfor amplification through PCR, as described herein. RT-PCR amplificationreactions can be carried out with a variety of different reversetranscriptases, and in various embodiments, a thermostablereverse-transcriptions can be used. Suitable thermostable reversetranscriptases can comprise, but are not limited to, reversetranscriptases such as AMV reverse transcriptase, MuLV, and Tth reversetranscriptase.

In various embodiments, assay can be an assay for the detection of RNA,including small RNA. Detection of RNA molecules can be, in variouscircumstances, very important to molecular biology, in research,industrial, agricultural, and clinical settings. Among the types of RNAthat are of interest in various embodiments are, for example, naturallyoccurring and synthetic regulatory RNAs such as small RNA molecules(Lee, et al., Science 294: 862-864, 2001; Ruvkun, Science 294: 797-799;Pfeffer et al., 304: Science 734-736, 2004; Ambros, Cell 107: 823-826,2001; Ambros et al., RNA 9: 277-279, 2003; Carrington and Ambros,Science 301: 336-338, 2003; Reinhart et al., Genes Dev. 16: 1616-1626,2002 Aravin et al., Dev. Cell 5: 337-350, 2003, Tuschel et al., Science294: 853-858, 2001; Susi P. et al., Plant Mol. Biol. 54: 157-174, 2004;Xie et al., PLoS Biol. 2: E104, 2004). Small RNA molecules, such as, forexample, micro RNAs (miRNA), short interfering RNAs (siRNA), smalltemporal RNAs (stRNA) and short nuclear RNAs (snRNA), can be, typically,less than about 40 nucleotides in length and can be of low abundance ina cell.

With appropriate detection probes, reader-analyzer instrument 176 candetect miRNA expression found in, for instance, cell samples taken atdifferent stages of development. In various embodiments, coexpressionpatterns can be analyzed across microfluidic card 174 with TaqMan®sensitivity, specificity, and dynamic range. In various embodiments,such methods obviate the need for running further assays to validate theexpression levels. In various embodiments, reader-analyzer instrument176 can be used to validate that siRNA molecules have successfully,post-translationally regulated the gene expression patterns of interest.In various embodiments, such methods may be useful during themanipulation of gene expression patterns using siRNAs in order toelucidate gene function and/or interrelationships amongst genes. Invarious embodiments, gene expression patterns can be introduced intoliving cells, cellular assays can be seen on reader-analyzer instrument176 and can reveal gene functions. In various embodiments, analysis forsmall RNA can be run on reader-analyzer instrument 176 allowing for ahigh number of simultaneous assays on a single sample with performancethat obviates the need for secondary assays to validate the geneexpression results.

In various embodiments, multiplex methods are provided wherein assaycomprises a first universal primer that binds to a complement of a firsttarget, a second universal primer that binds to a complement of a secondtarget, a first detection probe comprising a sequence that binds to thesequence comprised by the first target, and a second detection probecomprising a sequence that binds to a sequence comprised by the secondtarget. In various embodiments, at least some of the plurality of wellsof comprise a solution operable to perform multiplex PCR. The first andsecond detection probes can comprise different labels, for example,different fluorophores such as, in non-limiting example, VIC and FAM.Sequences of the first and second detection probes can differ by aslittle as one nucleotide, two nucleotides, three nucleotides, fournucleotides, or greater, provided that hybridization occurs underconditions that allow each detection probe to hybridize specifically toits corresponding detection probe.

In various embodiments, multiplex PCR can be used for relativequantification, where one primer set and detection probe amplifies thetarget DNA and another primer set and detection probe amplifies anendogenous reference. In various embodiments, the present teachingsprovide for analysis of at least four DNA targets in each of theplurality of wells and/or analysis of a plurality of DNA targets and areference in each of a plurality of wells in microfluidic card 174.

In various embodiments, DNA applications such as, for example, PCR, maybe detected using electrochemical detection methods. In variousembodiments, a hand held pathogenic detection device utilizeselectrochemical detection. In various embodiments, such electrochemicaldetection methods employ Taq polymerase and 5′-exonucleoase activitypreamplification, as described below. In such electrochemical detection,the use of a fluorescent probe as described above may not be needed. Invarious embodiments, during the PCR extension step, a unique oligo probemay be cleaved by attack polymerase after completion of the PCR, thereleasable oligo probe may be hybridized to a capture anti-sense oligoimmobilized on the surface of the electrochemical detector. In variousembodiments, the oligo probe which can be hybridized to a surface of theelectrochemical detector may generate a yes answer and lack ofhybridization may generate a no answer for the target related to apathogen or virus for which it is being analyzed. In variousembodiments, such a handheld may be able to multiplex several targets bydesigning a multiple of unique probes that may hybridize to a uniquedetector thus providing a yes/no answer for each of multiple targets fora group of pathogens or viruses being analyzed. Examples of use of DNAamplification assay employing electrochemical detection may be found inU.S. Provisional Patent Application No. 60/699,950, filed Jul. 7, 2005and commonly assigned.

An example of a portable reader-analyzer instrument 54 is illustrated inFIG. 14 as an exemplary device. In various embodiments, portableinstrument 54 can utilize six fluidic cartridges 322. The portableinstrument 54 can be capable of analyzing a variety of different sampletypes. In various embodiments, it may be powered using AC power from astandard outlet, or by battery power, or by solar power. Further, theportable instrument 54 can have no processing ability, relying insteadon connection to, and control by, a computer. This computer can be alaptop, so not to limit the portability of the portable instrument 54.In various embodiments, the portable instrument 54 can be configured todetect on the order of ten strains of bacteria or virus (althoughsmaller and larger numbers of strains are also possible).

In various embodiments, the portable instrument 54 employs up toapproximately 50 detection wells and can be capable of analyzingmultiple samples per run. In various embodiments, portable instrument 54can be configured to perform multiplex PCR (as discussed above) in atleast one pre-filled reagent cartridges 336. For example, with 50detection wells, five patient samples could be analyzed for ten agentseach. One or more of the agents can be controls, which are used tocalibrate the portable instrument. Calibration is discussed in moredetail below. FIG. 15 illustrates a cutaway view of a fluidic cartridge322, including a cartridge housing 338, a flexible printed circuit board(PCB) interconnect 332, a sample inlet 334 for a 5 mL syringe 330, andpre-filled reagent cartridges 336. In various embodiments, portableinstrument 54 may perform PCR utilizing electrochemical detection.

FIG. 16 shows a handheld instrument 56. The handheld instrument 56 canbe configured to analyze a single sample per run. For example, thissample can be a sputum sample from a patient. The handheld instrument 56can be pocket-sized or about the size of a deck of playing cards toallow for easy storage and portability. To this end, the processingability of the handheld instrument 56 may be limited, relying onexternally located resources for more sophisticated analysis.

The handheld instrument 56 can be configured to detectmulti-drug-resistant tuberculosis, a very useful application indeveloping countries. The handheld instrument 56 may be capable ofrunning on batteries for situations where electrical power may be notpresent or may not be reliable. An internal controller can automaticallycoordinate transfer of data acquired by the handheld instrument 56 toanother device for further analysis such as P2P communication to anotherhandheld instrument 56, a portable instrument 54, a laboratoryinstrument 52, a local computer, a network, or a distant server. Suchcommunication may be wired, wireless, or a combination thereof. Invarious embodiments, handheld instrument 56 may include a GPS device toidentify the location of the where the sample was analyzed and suchresulting spatial data can be communication along with PCR results forfurther analysis.

Referring to FIG. 17, components of handheld instrument 56 can includean enclosure back 360, microfluidic device 362, an enclosure front 364,and an activation slider 366. FIG. 18 demonstrates the activation of thehandheld instrument 56 by sliding the activation slider 366 in thedirection of the arrow toward the top of the handheld instrument 56.FIG. 19 demonstrates protusions 380 on the inside of the activationslider 366 that activate the PCR process. In various embodiments,handheld instrument 56 can be preloaded with PCR reagents. In variousembodiments, handheld instrument 56 may perform PCR utilizingelectrochemical detection, as discussed above.

Although not specifically illustrated, each of the above reader-analyzerinstruments 176 can be provided with a visual readout. This readout canbe used to display operating instructions or messages to the user,including alert messages about tests that should be performed on thereader-analyzer instrument 176. Such messages would be provided usingthe communication capability of the instrument. In various embodiments,reader-analyzer instrument 176 can include a MMI such as, for example, akeyboard. The MMI may be useful for entering spatial and/or demographicdata, and/or confirming each step performed during an analysis, and/orto communicate with the network, a computer or another reader-analyzerinstrument 176. In various embodiments, the MMI can be a computer inbi-directional communication with reader-analyzer instrument 176. Invarious embodiments, cellular phone capabilities may be included in thereader-analyzer instrument 176. In various embodiments, thereader-analysis instruments 176 utilize a common genetic assay analysisplatform, such as a TaqMan® assay-based platform, as discussed herein,which utilizes PCR techniques. A collection 102 of reader-analyzerinstruments 176 may include other types of analysis platforms canadditionally or alternatively be used, such as, for example,hybridization array (microarray) platforms. In some applications, it maybe beneficial to utilize both hybridization array and PCR platformstogether. For example, a hybridization array technology can be employedfirst to screen a sample over a large number of different targets (e.g.,different bacteria, viruses, pathogens, and/or other target sequences).

In various embodiments, the results of the initial hybridization arrayanalysis can then indicate a PCR analysis to select for subsequenttesting. As will be more fully explained herein, the reader-analyzerinstruments 176 can be equipped with bidirectional communicationcapability such as P2P or through a network, and this communicationcapability can be utilized, for example, to send control instructionsand/or data from a hybridization array system to the PCR system, so thatthe PCR system will know, as identified by hybridization array system,what specific bacteria, virus, pathogen, and/or target sequence totarget.

Environmental samplers 60, 62, such as, for example, air sampler 60 andwater sampler 62, can be used. In various embodiments, data collectedfrom any environmental samples can be included in the spatial data thatis uploaded to the system 10. In various embodiments, theseenvironmental samplers 60, 62 can simply be a front-end to the PCRprocess detailed above, containing an apparatus to capture a sample, andsuspend it in solution for processing by an ISAP module 172. In variousembodiments, the samplers 60, 62 can include specific PCR instrumentsdesigned to perform PCR analysis on environmental samples. Similarly,PCR can be employed in medical diagnostics, environmental studies,clinical studies, food/agricultural analysis, animal/organism testing,and chemical content analysis.

In various embodiments, PCR can be adapted to perform quantitative PCR.In various embodiments, two different methods of analyzing data from PCRexperiments can be used: absolute quantification and relativequantification. In various embodiments, absolute quantification candetermine an input copy number of the target DNA of interest. This canbe accomplished by relating a signal from a detection probe to astandard curve. In various embodiments, relative quantification candescribe the change in expression of the target DNA relative to areference or a group of references such as, for example, an untreatedcontrol, an endogenous control, a passive internal reference, auniversal reference RNA, or a sample at time zero in a time coursestudy. When determining absolute quantification, the expression of thetarget DNA can be compared across many samples, for example, fromdifferent individuals, from different tissues, from multiple replicates,and/or serial dilution of standards in one or more matrices.

In various embodiments of the present teachings, PCR can be performedusing relative quantification and the use of standard curve may not berequired. Relative quantification can compare the changes in steadystate target DNA levels of two or more genes to each other with one ofthe genes acting as an endogenous reference, which may be used tonormalize a signal from a sample gene. In various embodiments, in orderto compare between experiments, resulting fold differences from thenormalization of sample to the reference can be expressed relative to acalibrator sample. In various embodiments, the calibrator sample can beincluded in each sample well of the assay panel. The analysis system candetermine the amount of target DNA, normalized to a reference, bydetermining

ΔC _(T) =C _(Tq) C _(Tendo)

where C_(T) is the threshold cycle for detection of a fluorophore inreal time PCR; C_(Tq) is the threshold cycle for detection of afluorophore for a target DNA in sample; and C_(Tendo) is the thresholdcycle for detection of a fluorophore for an endogenous reference or apassive internal reference in assay. In various embodiments, a geneexpression analysis system can determine the amount of target DNA,normalized to a reference and relative to a calibrator, by determining:

ΔΔC _(T) =ΔC _(Tq) −ΔC _(Tcb)

where C_(Tq) is the threshold cycle for detection of a fluorophore forthe target DNA in sample; C_(Tcb) is the threshold cycle for detectionof a fluorophore for a calibrator sample; ΔC_(Tq) is a difference inthreshold cycles for the target DNA and an endogenous reference; andΔC_(Tcb) is a difference in threshold cycles for the calibrator sampleand the endogenous reference If ΔΔC_(T) is determined, the relativequantity of the target DNA can be determined using a relationship ofrelative quantity of the target DNA, which can be equal to 2^(−ΔΔC)_(T). In various embodiments, ΔΔC_(T) can be about zero. In variousembodiments, ΔΔC_(T) can be less than ±1. In various embodiments, theabove calculations can be adapted for use in multiplex PCR (See, forexample, Livak et al. Applied Biosystems User Bulletin #2, updatedOctober 2001, and Livak and Schmittgen, Methods (25) 402-408 (2001). Invarious embodiments, once calibration has been performed on all targetsequence C_(T) values to produce their respective copy numbers, the datacan be analyzed in various ways.

A knowledge base comprises a set of sentences (or rules, etc.) thatassert something about the context within which they exist. For example,in the real-time PCR context, asserting that “a C_(T) value less thantwenty for a target sequence X means the target sequence level of X ishigh” is an application of knowledge that originates from data in theEpiMonitor domain. The knowledge being represented in this example iswhen a target sequence level is high. Knowledge base constructioninvolves structuring the domain so that knowledge-creating methods orrules, which end users may devise, provide a framework for inference.

In various embodiments, a rules engine is described to flexibly createand process rules to apply a qualitative label or labels to quantitativeresults. Rules may be defined a priori by a user, or determined by therules engine based upon a learning algorithm. A simple example of a ruleis the application of a PLUS label when a C_(T) value is less than 30and a MINUS label when the C_(T) value is greater than or equal to 30.Such a simple inequality may not fully encapsulate the logical procedurea skilled user would undertake to reach a qualitative result. Forexample, a user can perform other evaluations related to real-time PCRexperiments to reach the conclusion that a PLUS label is appropriate.These evaluations include assessing data validity by looking at reactioncontrols, using quality control (QC) metrics to determinereproducibility, and looking at the C_(T) data to see if the value fallswithin an expected numerical range.

Each of the steps in the process can be defined using first-order logicto automate the application of a qualitative label to quantitative data.This definition can be achieved by codifying each of the process stepsas a Rule composed of Statements, building a ruleset that is a series ofthese rules, and examining the data against the ruleset (instantiation).Additional logical steps can be performed in the form of a decision treeor a forward- or backward-chaining program.

In various embodiments, a Pathogen Calculator software tool canimplement such a rules engine, which can apply a label of high, medium,or low to PCR data. Additionally, error labels such as invalid,unrepeatable, or out of bounds can be applied. A label of invalid can beapplied if measurement of reaction controls indicates a failure occurredin the reaction process. A label of unrepeatable can be applied if thedata does not meet QC metrics, such as records of time and temperaturerecorded by the instrument performing PCR. A label of unrepeatable canalso be applied if statistical parameters of the data, such as standarddeviation, are outside of permissible boundaries. A label of out ofbounds can be applied if the C_(T) value is less than a lower limit,indicating too much fluorescence (or other indicator) at too early astage, and thus invalid data. The label of out of bounds can also beapplied if the C_(T) value is too great, indicating a result beyond theaccepted resolution of the instrument.

The Pathogen Calculator tool can define certain thresholds based uponthe sample card 174 configuration to flag percentages, quantities,and/or qualitative results. Threshold violations and other results,whether qualitative or quantitative, can be demonstrated graphically tothe user. In various embodiments, the Pathogen Calculator includes apercentage calculator that can be used to determine respectivequantities of the various target sequences present. The target sequencepercentage can be calculated by dividing the copy number of a selectedtarget sequence by the sum of all target sequence copy numbers, thenmultiplied by 100%. This information can be displayed in various ways,including tables and bar charts. In various embodiments, the PathogenCalculator tool may be implemented within one of the reader-analyzerinstruments 176, or within a computer in communication with thereader-analyzer instrument 176. Data, qualitative or otherwise, that isgenerated by the Pathogen Calculator can be communicated to theEpiMonitor software platform 100, instead of, or in addition to, thereaction data. In various embodiments, the Pathogen Calculator can belocated in a reader-analyzer instrument 176, or in a computer associatedwith a reader-analyzer instrument 176. The Pathogen Calculator can alsobe implemented in the EpiMonitor software platform 100 itself.

Because C_(T) values may vary based upon a number of factors, includingthe reader-analyzer instrument 176 platform type, the assay type, andthe genetic material sample type, a rules engine can take these factorsinto account. For instance, different rulesets can be defined for eachplatform, such as one ruleset for a BioRad LightCycler, and another foran ABI 7900. Within each platform ruleset, there can be groups of rulesfor each sample type, such as blood, sputum, hair, dirt, saliva, etc.Each group of sample type rules can contain individual rules for eachassay type, such as a particular manufacturer's primer/probe set usedfor detecting bordetella pertussis. This linear model can beextrapolated to greater or fewer numbers of factors.

Other rules may be included for each individual target sequence, forexample, different target sequences out of each of two pathogenic E.coli strains, such as O127:H7 and O157:H7. Still other rules can includenormalizing to a variety of different endogenous controls that can beused in individual assays. Combinations of all or a subset of theserules can be used in various embodiments. Standardized chemistry andcontrols can be used to help limit the amount of rules to a manageablenumber.

In various embodiments, hierarchical rules can be defined. For example,a ruleset can be defined for platform type, a ruleset can be defined forsample type, and a ruleset can be defined for assay type. These rulesetscan then be applied serially. For example, rules within the platformtype ruleset can be applied based upon the type of platform used toacquire PCR data. Then rules within the sample type ruleset can beapplied based upon the type of sample from which genetic information wasextracted. Then rules within the assay type ruleset can be applied basedupon the assay type, for example, controls, PCR chemistry, probes, etc.

In various embodiments, a global ruleset can be defined that operates onnormalized values, whether normalized C_(T) values, normalized copycount numbers, or other suitable values. Normalization, as describedbelow, can account for variations in factors such as platform type,sample type, and assay type. Then a global ruleset can be appliedequally to the normalized numbers, regardless of platform, sample type,assay type, etc.

The following exemplary XML (extensible markup language) codedemonstrates a data structure containing rules that can be passed to thePathogen Calculator. This data structure can be stored within theEpiMonitor platform 100 or communicated to reader-analyzer instruments176. These rules can be used by the Pathogen Calculator to qualitativelylabel quantitative results from a real-time PCR run.

<?xml version=″1.0″ ?> − <qualresult type=”linearrulesresult”> −<linearrulesresult owner =”B-pert” sample_name=”Sample01”description=”General Linear Evaluation”> − <rulesresult owner =”B-pert”sample_name=”Sample01” description=”Poor Replicate Data Quality”true_result=”Pathogen QC: Fail” false_result=”ok” true_color=”205,92,92”false_color=”152,251,152” operators=”NONE”> − <rule owner =”B-pert”sample_name=”Sample01” operators=”AND” datatype=”StdDev(Ct)”> <statementoperator=”GREATER THAN” value=”2” /> <statement operator=”NOT EQUAL”value=”NaN” /> </rule> </rulesresult> − <rulesresult owner =”B-pert”sample_name=”Sample01” description=”Low Pathogen Quality”true_result=”low” false_result=”not low” true_color=”152,251,152”false_color=”205,92,92” operators=”NONE”> − <rule owner =”B-pert”sample_name=”Sample01” operators=”OR” datatype=”Mean(Ct)”> <statementoperator=”GREATER THAN” value=”35” /> <statement operator=”EQUAL”value=”NaN” /> </rule> </rulesresult> − <rulesresult owner =”B-pert”sample_name=”Sample01” description=”Medium Pathogen Quality”true_result=”medium” false_result=”not medium” true_color=”238,221,130”false_color=”152,251,152” operators=”NONE”> − <rule owner =”B-pert”sample_name=”Sample01” operators=”AND” datatype=”Mean(Ct)”> <statementoperator=”LESS THAN” value=”35” /> <statement operator=”GREATER THAN”value=”25” /> </rule> </rulesresult> − <rulesresult owner =”B-pert”sample_name=”Sample01” description=”High Pathogen Quality”true_result=”high” false_result=”not high” true_color=”238,92,66”false_color=”152,251,152” operators=”NONE”> − <rule owner =”B-pert”sample_name=”Sample01” operators=”AND” datatype=”Mean(Ct)”> <statementoperator=”LESS THAN” value=”25” /> </rule> </rulesresult></linearrulesresult> </qualresult>

This data structure defines a decision tree type of analysis for theB-pert assay for a sample named Sample01. Each rule is evaluated inorder until a true result is found. The first rule defines “PoorReplicate Data Quality.” This rule states that the replicate data ispoor when this target sequence's standard deviation of C_(T) is greaterthan 2 and not equal to “NaN.” When true, the rules engine will returnthe qualitative result “Pathogen QC: Fail,” which denotes a failure inthe real-time PCR.

The second rule defines a “Low Pathogen Quantity,” which is present whenan arithmetic mean of C_(T) values is greater than 35 or equal to “NaN.”This will return a qualitative result of “Low” when true. The third ruledefines a “Medium Pathogen Quantity,” which is expressed by amean(C_(T)) less than 35 and greater than 25. This will return aqualitative result of “Medium” when true.

The last rule is a “High Pathogen Quantity,” which is expressed by amean C_(T) value less than 25. This will return a qualitative result of“High” when true. This example demonstrates how knowledge of assayparameters can be codified, in this case knowledge of the Bordetellapertussis assay, and what sorts of qualitative results can be generated.

A ruleset data structure can be encoded to be easily readable by bothhumans and computer programs such as by using XML, as demonstratedabove. Such a ruleset may be coded in any machine-readable language. Inaddition to the qualitative results returned by the decision tree, eachrule can return other types of data such as strings (in this case, theserules also return text indices for RGB color in order to give a colorrepresentation along with the qualitative result), other rules, or othersets of rules.

Normalization allows data to be compared without regard to systematicvariations. Such systematic variations include differences betweenplatforms, between different sample types, and between different assaytypes. Each machine that performs PCR may have slightly differentoperating parameters, and differences between manufacturers may be evengreater. Various sample types entail differences in the difficult ofpurifying the nucleic acid content in the sample, whether and to whatextent PCR inhibitors are present, and quantity of nucleic acid pervolume. Different assay types produce different reaction rates, and eachmay interact with a sample differently. The linear rules engine modeldescribed above is one approach to normalization. By generating aqualitative tag for each set of reaction data, disparate reaction datacan be compared, regardless of PCR platform, symptoms, illness, etc. Thenormalization is accomplished by having rules specifically tailored toeach combination of variable, such as assay X, taken on instrument Y,originating from sample Z.

Quantitative normalization is also possible. One approach is to convertC_(T) to a genomic copy number. This conversion can be accomplishedthrough the use of absolute or relative quantitation. Relativequantitation relies on comparing the fluorescence (or other indicia) ofprobes for the target sequence of interest to fluorescence (or otherindicia) of probes for a genetic standard within the same reaction well.This standard can be genetic data assumed to be present in substantiallyconsistent quantity (such as GAPDH, discussed below), or added to thesample. Absolute quantitation relies on forming a standard curve for anassay via a dilution series prepared a priori. The dilution seriesrecords fluorescence (or other indicia) data (often measured by C_(T))at various starting copy numbers of the target sequence of interest.Then, a linear best fit is determined for C_(T) vs. the logarithm (suchas base 10) of copy number, yielding a line described by a slope andy-intercept. Unknown C_(T)s (those measured in the field) can beconverted to copy number by interpolating the value from this line.

Each PCR assay and instrument platform can be described by standardcurve parameters that convert threshold cycle to copy number. This copynumber is then comparable across assays and instrument platforms. Copynumber can further be normalized against sample type by adjusting to astandard sample type, such as blood. A similar procedure could be used,wherein levels of known genetic sequences are measured within each ofthe various sample types, such as blood and sputum. A correlation, suchas a best-fit line, can then be fitted to the plot of copy number ofeach sample type of interest to copy number of the standard sample type.In various embodiments, triple delta C_(T), or delta delta C_(T),described below, can be used to normalize reaction data in the geneexpression domain.

Benefits of normalization can include, for example, system 10 is notreliant on just one or two types of reader-analyzer instruments 176, orreader-analyzer instruments 176 exclusively from one manufacturer, orusing one type of chemistry. Such benefits allow EpiMonitor softwareplatform 100 to encompass a greater universe of reader-analyzerinstruments 176 without additional capital expenditures or majorinstrument replacement, and thus allows for a greater quantity of datato be captured and participation of a larger group of labs.

In various embodiments of the present teachings, an analysis system canuse ΔΔC_(T) values computed for the same target DNA but in differentsamples (Sample A (S_(A)) and Sample B (S_(B))) in order to determinethe accuracy of subsequent relative expression computations. Thisresults in the equation as shown in FIG. 20,

ΔΔΔC _(T) T _(x) =ΔΔC _(T) T _(x) S _(A) −ΔΔC _(T) T _(x) S _(B)

In various embodiments, a value for ΔΔΔC_(T)T_(x) can be zero, orreasonably close to zero, which can indicate that the preamplifiedΔC_(T) values for T_(x) (ΔC_(T preamplified) T_(x)S_(A) andΔC_(T preamplified) T_(x)S_(B)) can be used for relative gene expressioncomputation between different samples via a standard relative geneexpression calculation. Such calculation may be useful in normalizingdata from different instruments 176 or as a QC step to accept or rejectnormalized data.

In various embodiments, a standard relative gene expression calculationcan determine the amount of the target DNA. In various embodiments, astandard relative gene expression calculation employs a comparativeC_(T). In various embodiments, the above methods can be practiced duringexperimental design and once the conditions have been optimized so thatthe ΔΔΔC_(T)T_(x) is reasonably close to zero, subsequent experimentsonly require the computation of the ΔC_(T) value for the preamplifiedreactions. In various embodiments, ΔΔC_(T)T_(x)S_(A) values can bestored in a database or other storage medium. In various embodiments,these values can then be used to convert ΔΔC_(Tpreamplified)T_(x)S_(A)values to ΔΔC_(T not preamplified)T_(x)S_(A) values. In variousembodiments, the ΔΔC_(T preamplified)T_(x)S_(y) values can be mappedback to a common domain. In various embodiments, a not preamplifieddomain can be calculated using other gene expression instrumentplatforms such as, for example, a microarray. In various embodiments,the ΔΔC_(T)T_(x)S_(A) values need not be stored for all different samplesource inputs (S_(A)) if it can be illustrated that theΔΔC_(T preamplified)T_(x) is reasonably consistent over different samplesource inputs.

In various embodiments, microarray technology, which can provide data tosystem 10. In various embodiments, a microarray can be a piece of glassor plastic on which single-stranded pieces of DNA are affixed in amicroscopic array as probes. In various embodiments, thousands ofidentical probes can be affixed at each point in the array which canmake effective detectors.

Typically, arrays can be used to detect the presence of mRNAs that mayhave been transcribed from different genes and which encode differentproteins. The RNA can be extracted from many cells, ideally from asingle cell type, then converted to cDNA. In various embodiments, thecDNA may be amplified in quantity by PCR. Fluorescent tags can beenzymatically incorporated into the or can be chemically attached tostrands of cDNA. In various embodiments, a cDNA molecule that contains asequence complementary to one of the probes will hybridize via basepairing to the point at which the complementary probes are affixed. Invarious embodiments, the point on the array can then fluoresce whenexamined using a microarray scanner. In various embodiments, theintensity of the fluorescence can be proportional to the number ofcopies of a particular mRNA that were present and calculates theactivity or expression level of that gene.

In various embodiments, a microarray can be, for example, a cDNA array,a hybridization array, a DNA microchip, a high density sequenceoligonucleotide array, or the like. In various embodiments, a microarraycan be available from a commercial source such as, for example, AppliedBiosystems, Affymetrix, Agilent, Illumina, or Xeotron. In variousembodiments, a microarray can be made by any number of technologies,including printing with fine-pointed pins onto glass slides,photolithography using pre-made masks, photolithography using dynamicmicromirror devices, or ink-jet printers. The lack of standardization inmicroarrays can present an interoperability problem in bioinformaticssince it can limit the exchange of array data.

In various embodiments, microarray output data can be in a format offluorescence intensity and in various embodiments, microarray outputdata may be in a format of chemiluminescence intensity. In variousembodiments, an intensity value from a microarray output data can beglobally normalized. In various embodiments, total difference values canbe determined by subtracting background noise and normalizing the arraysignal intensity, then dividing experimental sample signal intensity bya control sample signal intensity yielding net sample intensity. Invarious embodiments, a control sample used to generate the controlsample signal intensity can be, for example, Stratagene®, UHR, or thelike. In various embodiments, a total difference can be converted to alog₂ by the following equation:

2^(ΔΔC) _(T)=3.3 log₁₀(net intensity sample 1/net intensity sample 2)

In various embodiments, microarray output data is in a ΔΔC_(T) format.In various embodiments, microarray output data can be converted into aΔΔC_(T) format by the following equation:

R=(½)^(ΔΔC) _(T)

where R is the resulting measurement from a microarray. Suchcalculations are available commercially, such as GeneSpring from SiliconGenetics. Various embodiments include converting microarray output datainto a ΔΔC_(T) format using a Global Pattern Recognition (GPR) algorithmwhich can convert intensity values generated from microarrays fromlinear values to algorithmic values and can use transformed intensitycutoffs to affect gene and normalizer filters. In various embodiments,GPR software algorithm may be available from The Jackson Laboratory. Invarious embodiments, microarray output data can be in a standardlanguage or format such as MAGE-ML (microarray and gene expressionmarkup language), MAML (microarray markup language), or MIAME (minimuminformation about microarray experiments). In various embodiments, suchstandardized formats and language can be converted to a ΔΔC_(T) format.

In various embodiments, microarray output data can be in a ΔΔC_(T)format, then PCR data can be directly compared to data from microarrayplatforms as shown in FIG. 21. In various embodiments, a ΔΔΔC_(T)calculation can be a validation tool to confirm that relativequantitation data can be compared from one amplification/detectionprocess to another. In various embodiments, ΔΔΔC_(T) calculation can bea validation tool to confirm that relative quantitation data can becompared from one sample input source to another sample input source,for example, comparing a sample from liver to a sample from brain in thesame individual. In various embodiments, ΔΔΔC_(T) calculation can be avalidation tool to confirm that relative quantitation data can becompared from one high-density sequence detector system to anotherhigh-density sequence detection system.

In various embodiments, MΔC_(T) calculation can be a validation tool toconfirm that relative quantitation data can be compared from oneplatform to another, for example, data from real time PCR to data from ahybridization array is especially valuable for cross-platformvalidation. In various embodiments, real-time PCR and hybridizationarray data can be directly compared. In various embodiments, a TaqMan®ΔΔC_(T) can be compared to a microarray output converted to the ΔΔC_(T)format. In various embodiments, the resultant ΔΔΔC_(T), if within +/−1C_(T) of zero, can determine a high-degree of confidence that the actualtotal difference observed within each of the two platforms iscorrelative and, as such, may be normalized for entry into system 10.Further discussion of ΔΔΔC_(T) can be found in commonly assigned U.S.patent application Ser. No. 11/086,253.

In various embodiments, a correction, which can be a quantity added to acalculated or observed value to obtain the true value, may be used sothat data generated on two different platforms can be used together infurther calculations and analysis. Various embodiments allow for largerand sometimes more complete data sets to be used in gene expressionstudies. In various embodiments, the correction can be calculated from aresulting ΔΔΔC_(T). In various embodiments, a correction can be a biascorrection.

Referring now to FIG. 22, a graphical representation illustrates some ofthe information that can be acquired and transmitted by thereader-analyzer instruments 176 to the EpiMonitor software platform 100.The reader-analyzer instruments 176 may provide PCR analysis results orother genetic data 452 to the EpiMonitor software platform 100. Theseanalysis results can be in the form of raw data, can be pre-processed insome respect, or can even be a diagnosis (such as a determination thatthe patient is infected with a particular illness). Pre-processingpossibilities are discussed in greater detail below.

EpiMonitor software platform 100 can also store an identifier for eachassay performed on a sample as part of the PCR analysis results or othergenetic data 452. This can be based upon Logical Observation IdentifiersNames and Codes (LOINC), a standard that codifies laboratory andclinical observations and can be applied by the EpiMonitor administratorwhen creating a panel and a probe (see below).

Reader-analyzer instruments 176 can also capture contextual information462, including spatial, temporal, climate, and priority information.Spatial (e.g., geographic) information describes where the sample wasobtained, where the sample was prepared for processing, and/or where thesample analysis was performed. When analyzing genetic material of anentire population (whether of a community, a country, the world, etc.),this spatial component is useful for such purposes as analyzing how atarget sequence is spreading through a population.

Spatial information can be provided by a user (including the patient orclinician) or automatically by the reader-analyzer instrument 176. Invarious embodiments, the reader-analyzer instrument 176 includes asystem for ascertaining its geographic or spatial position. This can beprovided by a GPS (Global Positioning System) device that is eitherembedded in, or in communication with, the reader-analyzer instrument176. Additionally, the location of the reader-analyzer instrument 176can be obtained by determining its IP (Internet Protocol) address andusing a suitable lookup table to convert the IP address into ageographic location. While IP addresses are not uniformly accurate asindicators of physical location, the EpiMonitor software platform 100can circumvent this limitation by requiring that the user or instrumentregister its geographic location once the instrument is connected to thecommunication network via the EpiMonitor software platform 100.

In addition to spatial information, temporal information can be retainedfor historical analysis. Reader-analyzer instruments 176 can synchronizetheir internal clocks with a reference clock of the EpiMonitor platform100 to ensure accurate temporal information. As time-correlatedhistorical data is accumulated, analysis can become increasinglypowerful. For example, more thorough “baselines” can be collected todiscern true signals from noise, and cyclical patterns may emerge thataid prediction or diagnosis.

Climate information can be useful to analyze results with regard toseasonal, weather, and/or pollution effects. Climate informationincludes temperature, humidity, precipitation, wind speed, and airquality. This information can be correlated with the temporalinformation to determine disease or other factors that might be moreclosely correlated with temperature than with season.

Priority level data can include information about conditions under whichthe reader-analyzer instrument 176 being used, as that information mightbe indicative of whether a positive detection of a particular bacteria,virus, pathogen or other target sequence should be used to trigger apublic health warning or other action. In this regard, a positivedetection from a single handheld instrument 56 might not warrant apublic health alert; however, a single report from a laboratoryinstrument 52 associated with a reference laboratory or nationallaboratory might well warrant a public alert. The priority level datacan be used to allow the laboratory response network 50 to interpret thereported information properly.

Subject information 472 can be recorded as well, and may includeidentification data, demographic data, diagnostic data, and clinicalobservations. Identification data, stored confidentially, is valuablefor a number of reasons. A patient whose biological sample is laterdetermined to contain a pathogen could be alerted to this fact. If thebiological sample was taken from an animal, the animal may need to bequarantined or put down. If an infected patient visits multiple clinicsor has multiple samples taken, it is useful to allow the system toidentify that each of the samples came from the same subject. Any samplesource can be recorded in the EpiMonitor software platform 100,including humans, animals, environmental samples, and plants. Apopulation that can be analyzed on the EpiMonitor software platform 100can be a group any living organism including plant for example filed ofGMO crops. Identification information may differ for different types ofsample sources, and such provision can be made in the database.

The Health Insurance Portability and Accountability Act (HIPAA) is thecode of national standards for protecting the privacy of personal healthinformation set forth by the U.S. Health and Human Services (HHS)department. In compliance with HIPAA, the EpiMonitor software 100 canstore a unique key assigned to the patient by the clinician orphysician. Certain applications, regulations, or privacy concerns maydictate that personal information not be obtained in particularcircumstances. Demographic data, such as age and gender of the patient,can be stored. Correlating this data may lead to determinations ofparticular susceptibility of a certain age group or gender to a certaintarget sequence.

Diagnostic data includes information provided by the patient andinformation determined by the clinician by observing or analyzing thehuman or animal subject. In the case of a human subject, a chiefcomplaint can be recorded. This is the complaint voiced by the patientand recorded by the clinician. The complaint can be coded by ICD-9(International Classification of Diseases, ninth revision), a uniformcode that can be used to tag each patient's syndrome or diagnosis (e.g.,fever=12345, cough=12346, etc.), or can be stored as a physician's freetext remarks (e.g., “fever,” “cough,” etc.). The physician's diagnosisof the patient can also be stored as an ICD-9 code or free text. Otherstorage possibilities are CPT (Current Procedural Terminology) code,commonly used for medical billing, and SNOMED (Systematized Nomenclatureof Medicine) clinical terms.

To standardize input arriving as free text, natural language processingtechniques can be used to convert free text into a code that can be usedby a computer. For example, if the chief complaint reads “cough,sneezing, some fever,” a text classifier can translate this into ICD-9code 122.3 (“Respiratory Illness with Fever”). If samples are fromplants and/or animals, observable characteristics can also be recorded.This can include, for instance, color, flowering patterns, yields,insect infestation, pesticide and/or herbicide application, and/orobserved resistance to disease/pesticides.

Referring now to FIG. 23, a database implementation capable of storingthe above information is depicted. In the exemplary databaseimplementation, a variety of interrelated tables can be used. Thesetables can include a Detection_Software_Type table 502 and a Panel table504, which links to a Device table 506 and the Detection_Software_Typetable 502. A Probe table 508 and a Probe_location table 518 link to thePanel table 504. An Instance table 510 also links to the Panel table504. A Sample table 512 links to the Instance table 510 and to a PatientInformation table 516. A Clinical Data table 514 and an Interp_Datatable 522 link to the Sample table 512. A Raw_data table 524 links tothe Interp_Data table 522. A Probedata table 520 and the Interp_Datatable 522 link to the Probe table 508, A Data Received table 526, an XMLException table 520, and a Users table 530 are unlinked.

The Data Received table 526 includes XMLData, Date_received, andChunk_num. The XML Exception table 528 includes XMLData, Date_received,Chunk_num, and XML_message. The Users table 530 includes Username, FullName, Role, and Date_created. The Detection_Software_Type table 502includes Name, Description, and Version.

The Panel table 504 includes Name, Description, Version, Create_Date,Device_id, which links to the Device table 506, andDetection_software_id, which links to the Detection_Software_Type table502. The Device table 506 includes Name and Description. The PatientInformation table 516 includes HIPAA_Patient_IDs, Date_of_birth, andSex. The Raw_data table 524 includes Interp_data_id, which links to theInterp_Data table 522, C_(T), Quantity, well_num, Reporter, and Task.

The Interp_Data table 522 includes Probe_id, which links to the Probetable 508, Agg_C_(T), Threshold, Sample_id, which links to the Sampletable 512, and interpolated_copy_num. The Probedata table 520 includesProbe_id, which links to the Probe table 508, Ct_mean, Ct_std, andQuantity. The Probe_location table 518 includes Panel_id, which links tothe Panel table 504, Probe_id, which links to the Probe table 508, andwell_num. The Probe table 508 includes Panel_id, which links to thePanel table 504, Description, calibration_slope, calibration_yint,detector_name, create_date, is_standard, and LOINC_code.

The Clinical Data table 514 includes Diagnosis ID, Diagnosis Type, ChiefComplaint, Chief Complaint Type, and Sample_id, which links to theSample table 512. The Sample table 512 includes Instance_id, which linksto the Instance table 510, Description, Sample_number, Name,Location_zip, Location_city, Location_state, and Patient_id, which linksto the Patient Information table 516. The Instance table 510 includesUpload_time, Location_zip, Location_city, Location_state, Panel_id,which links to the Panel table 504, file_version, and date_received. ThePanel table 504 includes Name, Description, Version, Create_Date,Device_id, which links to the Device table 506, andDetection_software_id, which links to the Detection_Software_Type table502.

The Data Received table 526 and XML Exception table 528 serve astemporary data stores for XML uploads used for authentication anddebugging. The Users table 530 contains the names of users allowedaccess to the system and the roles, or user access rights, that theyhave (discussed in more detail below). Mapping the data types of FIG. 23to those adopted by other institutions, such as the Public HealthInformation Network (PHIN) allows others, such as the CDC, toincorporate EpiMonitor data into their analysis.

The EpiMonitor platform 100 allows users to log in to the system anddefine assay panels, configure individual gene probes, and view uploadedinstances. Instance is the term used for the information related to thePCR analysis of a biological sample. A web interface provides aconvenient and widely accessible mode of operation. In variousembodiments, the EpiMonitor web interface includes a home page thatprovides a navigation index of other pages, including panels, probes,PCR devices, detection software types, and instances.

The home page can also display whether there are any identifiedoutbreaks or system warnings. Statistics can also be displayed regardingtiming of data uploads to the system, such as time of last upload,number of uploads for the current day, and total number of uploads.Clicking on the panel's link displays a list of currently definedpanels. Clicking on one of the panels produces a “view panel” page. Theinformation including the panel includes a description, a uniqueidentifier, a version number, the number of wells per sample, the PCRdetection software type (such as Sequence Detection Systems v. 2.2), theassociated PCR device type, the date of panel creation, and the probesassigned to the panel. The view panel page can also display the uploadedinstances that were based upon this panel.

A possible XML data structure that describes the panel is shown withexemplary data:

<?xml version=“1.0” ?> − <panel id=“3” description=“Tony's SmallerPanel” device_name=“7900” device_description=“AB7900”software_name=“SDS” software_version=“2.2” version=“1.0”wellspersample=“48” create_date=“2005- 07-22 07:07:02”> <probe id=“9”name=“B-pert” slope=“−3.3547” yint=“35.9770” description=“Bordetellapertussis” create_date=“2005-07-22 07:07:02” is_standard=“false” /><probe id=“10” name=“M-pneu” slope=“−3.4563” yint=“43.9540”description=“Mycoplasma pneumoniae” create_date=“2005-07-22 07:07:02”is_standard=“false” /> <probe id=“11” name=“S-pneu” slope=“−3.5956”yint=“43.0410” description=“Streptococcus pneumoniae”create_date=“2005-07-22 07:07:02” is_standard=“false” /> − <probeid=“12” name=“IPC” slope=“0” yint=“0” description=“Internal PositiveControl” create_date=“2005-07-22 07:07:02” is_standard=“true”><probedata probeid=“9” quantity=“0.10” ctmean=“27.6030” ctstd=“0.4180”/> <probedata probeid=“9” quantity=“1” ctmean=“24.6630” ctstd=“0.2350”/> <probedata probeid=“9” quantity=“10” ctmean=“21.0340” ctstd=“0.1840”/> </probe> </panel>

A subset of this data is transferred to the reader-analyzer instrument176 so that it can accurately report back the PCR data. An AssayInformation File (AIF) includes the sample name, detector, task (eithera standard or an unknown), and copy number quantity (known a priori forstandards) for each well of the microfluidic card 174. The first tenrows of an exemplary AIF are depicted in Table 3:

TABLE 3 Well Sample Name Detector Task Quantity 1 Sample01 B-pert UNKN 02 Sample01 C-pneu UNKN 0 3 Sample01 L-pneu UNKN 0 4 Sample01 M-pneu UNKN0 5 Sample01 C-botu UNKN 0 6 Sample01 S-pneu UNKN 0 7 Sample01 M-cataUNKN 0 8 Sample01 N-meni UNKN 0 9 Sample01 H-infl UNKN 0 10 Sample01S-aure UNKN 0

In the database, each probe can be defined by a number of criteria. Byclicking on one of the probes listed in the view panel page, arespective view probe page appears. The probe information includes adescription, a unique identifier, a calibration slope and y-intercept,the detector name, whether the probe is a standard, and creation date.Probe data is stored to allow conversion from C_(T) to copy number. Thisprobe data can be stored and viewed in a table with columns for copynumber (or quantity), mean value of C_(T), and standard deviation ofC_(T). The view probe page can also display the panels that employ thisprobe.

The view instance page, which can be accessed from a listing ofinstances linked to from the home page, or from one of the instanceslisted in a view panel page, indicates which samples correspond to theinstance. Instance information includes upload time, upload location(such as city, state, and zip code), version number, and time received.The view instance page can indicate which panel this instance used inperforming PCR. Further, a list of samples corresponding to thisinstance is presented.

A possible XML data structure for storing instance data is presentedwith exemplary data:

<?xml version=″1.0″ ?> − <epimonitor time=″2005-08-01 09:09:20″ zipcode=″94404″ version=″1″> <sample number=″1″ name=″Sample01″description=″Sample01″ zipcode=″94404″ /> <sample number=″2″name=″Sample02″ description=″Sample02″ zipcode=″94404″ /> <samplenumber=″3″ name=″Sample03″ description=″Sample03″ zipcode=″94404″ /><sample number=″4″ name=″Sample04″ description=″Sample04″zipcode=″94404″ /> <sample number=″5″ name=″Sample05″description=″Sample05″ zipcode=″94404″ /> <sample number=″6″name=″Sample06″ description=″Sample06″ zipcode=″94404″ /> <samplenumber=″7″ name=″Sample07″ description=″Sample07″ zipcode=″94404″ /><sample number=″8″ name=″Sample08″ description=″Sample08″zipcode=″94404″ /> − <panel id=″1″ detection_software_id=″0″device_id=″0″> <probe id=″2″ name=″B-pert″ slope=″−3.3547″ yint=″35.977″is_standard=″false″ /> <probe id=″3″ name=″L-pneu″ slope=″−3.1568″yint=″41.495″ is_standard=″false″ /> <probe id=″4″ name=′M-pneu″slope=″−3.4563″ yint=″43.954″ is_standard=″false″ /> <probe id=″5″name=″S-pneu″ slope=″−3.5956″ yint=″43.041″ is_standard=″false″ /><probe id=″6″ name=″M-cata″ slope=″−3.3986″ yint=″43.766″is_standard=″false″ /> <probe id=″7″ name=″N-meni slope=″−3.6138″yint=″45.285″ is_standard=″false″ /> <probe id=″8″ name=″S-aure″slope=″−3.4853″ yint=″45.472″ is_standard=″false″ /> − <probe id=″1″name=″IPC″ slope=NaN″ yint=″NaN″ is_standard=″true″> <probedataquantity=″0.1″ ctmean=″27.603″ ctstd=″0.418″ /> <probedataquantity=″1.0″ ctmean=″24.6663″ ctstd=″0.235″ /> <probedataquantity=″10.0″ ctmean=″21.034″ ctstd=″0.184″ /> </probe> </panel> −<datum samplenumber=″1″ probeid=″2″ ct=″17.651617″ threshold=″0.2″quantity=″290134.75″> <sdsfile ct=″19.225842″ quantity =′NaN″task=″Unknown″ reporter=″FAM″ well_num=″1″ /> <sdsfile ct=″16.077393″quantity=″NaN″ task=″Unknown″ reporter=″FAM″ well_num=″25″ /> </datum> −<datum samplenumber=″2″ probeid=″2″ ct=″22.861122″ threshold=″0.2″quantity=″8122.7397″> <sdsfile ct=″26.811136″ quantity=″NaN″task=″Unknown″ reporter=″FAM″ well_num=″49″ /> <sdsfile ct=″18.911108″quantity=″NaN″ task=″Unknown″ reporter=″FAM″ well_num=″73″ /> </datum> −<datum samplenumber⁼″3″ probeid=″2″ ct=″22.423084″ threshold=″0.2″quantity=″10971.777″> <sdsfile ct=″NaN″ quantity=‘ NaN″ task=″Unknown″reporter=″FAM″ well_num=″97″ /> <sdsfile ct=″22.423084″ quantity=″NaN″task=″Unknown″ reporter=″ FAM″ well_num=″121″ /> </datum> − <datumsamplenumber=″4″ probeid=″2″ ct=″25.692785″ threshold=″0.2″quantity=″1163.0924″> <sdsfile ct=″NaN″ quantity=″NaN″ task=″Unknown″reporter=″FAM″ well_num=″145″ /> <sdsfile ct=″25.692785″ quantity=″NaN″task =″Unknown″ reporter=″FAM″ well_num=″169″ /> </datum> − <datumsamplenumber=″5″ probeid=″2″ ct=″28.389887″ threshold=″0.2″quantity=″182.65738″> <sdsfile ct=″NaN″ quantity=″NaN″ task=″Unknown″reporter=″FAM″ well_num=″193″ /> <sdsfile ct=″28.389887″ quantity=″NaN″task=″Unknown″ reporter=″FAM″ well_num=″217″ /> </datum>

Clicking on one of the samples from the view instance page calls up aview sample page. The view sample page includes the number of thesample, the name and description of the sample, and the instance towhich the sample corresponds. The sample data can be presented in atable, listed by probe name. The threshold, C_(T) value, and computedcopy number are presented for each probe.

This web interface can be implemented with hierarchical access rightsgranted to different users. A class called Administrators can create andedit panels, probes, devices, and instruments. Administrators and alower privileged group, Viewers, can view the panel, probe, device, andinstrument data, as well as the C_(T) information collected by thesedevices. Data deemed proprietary, such as calibration parameters, couldbe hidden from various users, and entered into the EpiMonitor databasedirectly from a private database, inaccessible even to Administrators.Instances can only be viewed, not edited, as they represent acquireddata, not settings.

Referring now to FIG. 24, a functional flow diagram of an exemplarycommunication strategy between the EpiMonitor server 550 and areader-analyzer instrument (EpiMonitor client) 176 is presented. In step552, the EpiMonitor client requests information from the EpiMonitorserver 550 through a web services method regarding the panel to be run,and the EpiMonitor server 550 sends the corresponding information backto the client. This information identifies probe data for each welllocation. The information returned by the EpiMonitor server 550 can beembodied in an Assay Information File (AIF), described above. This panelinformation can be used in sample preparation to generate a microfluidiccard 174 arranged according to the panel information.

Web services provide a standard means of interoperating betweendifferent software applications running on a variety of platforms and/orframeworks. Web services are characterized by their greatinteroperability and extensibility, and can be combined in a looselycoupled way in order to achieve complex operations. Programs providingsimple services can interact with each other in order to deliversophisticated services. With web services, methods on other computersystems can be invoked through a request over HTTP. The web services canbe accessed in a variety of ways, including over the public Internet,Virtual Private Networks, and private networks. In addition, data can bepassed through HTTP, structured as an XML

In step 554, after PCR has been performed, raw data is available. Insome embodiments, a cycle threshold (C_(T)) can be computed for eachprobe and sample combination by detection software. This data can befurther processed by the EpiMonitor client. For example, if more thanone C_(T) value exists for a probe and sample combination (when thereare replicates, for example), an aggregate statistic (mean, median,standard deviation, etc.) can be determined instead of reporting eachC_(T) value. In step 556, the client calls a web services method on theEpiMonitor server 550 to obtain target sequence panel data.

If implemented, the Pathogen Calculator, as described above, receivesC_(T) values in step 558. Based upon probe calibration parameters, thePathogen Calculator can transform C_(T) values into copy numbers. Thecalibration parameters can include parameters describing a linearrelationship between C_(T) value and copy number, such as a slope and anintercept. The Pathogen Calculator can also perform other analysis oftarget sequence presence in a sample. A flexible rules-based embodimentof qualitative analysis performed by the Pathogen Calculator isdescribed above. This analysis can be provided to a user of the clientdevice and/or communicated to the EpiMonitor server 550.

In step 560, PCR data (e.g., C_(T) data, raw fluorescence or otherTaqMan® data), copy number and other information computed by thePathogen Calculator, corresponding panel information, andspatial/temporal/subject context information are integrated into an XMLdata structure by the EpiMonitor client. This data can be encoded andformatted according to Health Level 7 (a Standards DevelopingOrganization accredited by the American National Standards Institute)standards. The data structure can also be encapsulated in a suitabletransmission protocol, such as the Simple Object Access Protocol (SOAP).

In step 562, the client calls the web services method on the EpiMonitorserver 550 that facilitates data upload, encrypts the XML data, anduploads the encrypted data. This data is logged by the EpiMonitor server550 as an instance of the panel. In various embodiments, a manuallyentered zip or postal code can serve as the spatial record, and the timeof execution of step 562 of the client can be recorded as the temporalcomponent of the instance. In step 564, the XML data is decrypted,authenticated, parsed by EpiMonitor 100, and loaded into the EpiMonitordatabase tables. In step 566, information from the database tables ismade available to external systems, such as those compliant with thePHIN (Public Health Information Network).

When transporting data, Secure Sockets Layer (SSL) can be used. Invarious embodiments, the encryption-decryption standard algorithm forSSL will be based on the RSA algorithm. A Public Key Infrastructure(PKI) can be used for end-user or nodal authentication. The PKI providesfor third party vetting of user identities. PKI arrangements enableusers to be authenticated to each other, and to use the information inidentity certificates (i.e., each other's public keys) to encrypt anddecrypt messages. Once authenticated, a symmetric key system can be usedto transmit data in which the EpiMonitor server 550 and clients share acommon encryption-decryption method outside of the public keyinfrastructure to provide a layer of greater security beyondauthentication. In various embodiments, a certificate is issued to eachend-user upon their receipt of an EpiMonitor client device.

If desired, the EpiMonitor software platform 100 can be configured toload, install, or download software to each client device. This softwarecan share a unique encryption key with the EpiMonitor for everytransaction. Resultantly, the key will differ from transaction totransaction, and from device to device. This encryption key can use thecurrent time, in milliseconds, to encode data differently every timedata is sent to the server, making it extremely difficult to intercept.

As discussed above, a hybridization array can be used to prescreen asample to focus PCR testing upon specific bacteria, viruses, pathogens,or other target sequences. PCR testing from one instrument 176 canlikewise be used in focusing the analysis of other instruments 176. Amessage from a client detecting a certain target sequence can be used bythe EpiMonitor software platform 100 to automatically configure otherclients to begin testing for this detected target sequence. Dependingupon the circumstances of the detection, all units, or only selectedones, could be given instructions to begin testing for that targetsequence. Thus, for example, detection of a pathogen at a local airportmight cause messages to be sent to other airports that are connected byflight path with that airport. In this way, the potential spread of anepidemic can be intelligently tracked without having to alert all labsthroughout the nation.

The communication capability of the EpiMonitor clients (reader-analyzerinstruments 176) can be employed in paradigms other than strictclient-server. Clients can communicate directly with other clients toprovide input on what target sequences to be on alert for. Environmentalmeasuring instruments, such as air sampler 60, can also be of assistancein this function. For example, if an air sampler 60 at a particularlocation begins measuring higher than normal concentrations of aparticular substance, the air sampler 60 could communicate with otherclients (reader-analyzer instruments 176) to alert their respectiveoperators that they should begin testing for presence of a respectivetarget sequence.

In most cases, the EpiMonitor clients provide processed cycle thresholdinformation to the EpiMonitor server 550. If desired, however,EpiMonitor software 100 allows for raw data obtained from PCR (such asoptical image or current flow information) to be transmitted. The server550 can send a message to the client that will cause that instrument totransmit its raw data to the database system. This might be done, forexample, when analytic techniques are desired that the individualinstrument is not equipped to perform. Moreover, because the EpiMonitorsoftware platform 100 can support peer-to-peer cooperation, oneinstrument 176 could send its raw data output to another instrument 176,allowing that other instrument 176 to perform the analysis. This mightbe done, for example, when a small handheld unit 56 that does not havethe sophisticated processing capability of a larger laboratoryinstrument 52 performs the original analysis.

FIG. 25 is a functional block diagram of EpiMonitor client and servercomponents used in aid of communication. Within a client system 602,data is acquired from PCR (such as fluorescence or current data) by aPCR data module 604. This data is transmitted to an XML processingmodule 606, which organizes the data according to rules from a datadescription module 608. The XML processing module 606 can also receivedata from a pathogen calculator module 607 and a context acquisitionmodule 609. The pathogen calculator module 607 receives PCR data fromthe XML module 606 (or possibly directly from the PCR data module 604),and can also receive calibration information from the XML module 606.The pathogen calculator module 607 can calibrate the PCR data, and canperform qualitative or quantitative analysis on the data before or aftercalibration.

The context acquisition module 609 determines contextual data (such asthe spatial/temporal/climate/priority data described with respect toFIG. 25 at 462). The data description module 608 can also provide datarules to a sample preparation module 610, which is in communication witha sample assay preparation device (not shown). In this way, the datadescription module 608 can control which samples are prepared for whichlocations within the microfluidic card 174, and thus the PCR data fromthe microfluidic card 174 can be correctly interpreted.

Once the data has been transformed into an XML-based description, theXML module 606 forwards it to a SOAP (originally, Simple Object AccessProtocol) encapsulation module 612. The SOAP encapsulation module 612operates as a protocol for exchanging XML messages between computers,primarily via HTTP (HyperText Transfer Protocol). The SOAP message(envelope) is encrypted in an encryption module 614 for transmissionover a communications network 616. The communications network 616 can bethe Internet, a private network, or any other suitable networkstructure, whether local- or wide-area. Before or after encryption, theXML data can be stored in a local storage module 618 for latertransmission or for on-board processing. The local storage module 618can also store logs of successful transmissions.

The PCR data module 604 and sample preparation module 610 include a datagathering group 620, while the XML module 606, data description module608, SOAP encapsulation module 612, encryption module 614, and localstorage module 618 include a client-synchronizing group 622. The datagathering group 620 and client-synchronizing group 622 can be locatedwithin a single device. Alternately, the client-synchronizing group 622can be implemented in a separate computer, which connects to the datagathering group 620 via a localized network, such as Bluetooth,Ethernet, or infrared. This configuration can be useful when the datagathering group 620 is desired to be as portable as possible, as extraprocessing can be off-loaded to a separate computer.

A user interface module 625 communicates with the pathogen calculatormodule 607, the context acquisition module 609, and the local storagemodule 618. The user interface module 620 can include a display, akeypad, a touchscreen, a keyboard, a mouse, and/or any other suitableforms of input and output. The user can provide the user interfacemodule 625 with instructions for where to send acquired data,information about the samples undergoing PCR analysis, and directionsfor further processing. The user interface module 625 can displayprocess control information, PCR results, and/or qualitative analysisdetermined by the pathogen calculator module 607.

Within a server system 630, a security/queue module 632 communicateswith the communications network 616. The security/queue module 632decrypts incoming data, validates data, and queues data for processingby a description module 634. The security/queue module 632 can also beresponsible for establishing a secure connection with the secureconnection module 614 of the client system 602. The description module634 parses the received data for storage in a database 636. This parsingcan include such functions as preprocessing (discussed below), naturallanguage processing (described above), and data type conversion. Oncedata is stored in the database 636, an analysis module 638 can identifytrends and determine if triggering conditions are present.

A web server 640 communicates with the database 636, and also with thecommunications network 616. If the communications network 616 is notconnected to the Internet, the web server 640 can also communicate withthe Internet. The web server 640 allows for remote control and viewingof the database 636 and control of the analysis module 638, as describedabove. The web server 640 can include data visualization andsummarization responses to queries, or can provide customizablereal-time streaming of data and alerts.

A data integrator module 642 communicates with the database 636. Beyondcontextual data obtained from the client system 602 is contextual datathat further describes the environment in which the sample was taken orthat the host existed in. This information includesenvironmental/climate data (such as that provided by the NationalOceanic and Atmospheric Administration), demographic data (such as thatprovided by the U.S. Census Bureau). Much of this data may be acquiredfrom data stores distributed throughout the internet or other computersystems. Patient information, such as hospital and/or doctor records,may also exist in a digital format on a computer system.

Additionally, confirmatory data may be generated after positive resultsare detected via PCR. Confirmatory data includes microbiology culturetests and genetic resequencing assays/instruments. After identificationof an agent via a rapid biological assay such as real-time PCR, aconfirmatory test can be performed using “gold standard” procedures suchas viral/microbial cultures or Applied Biosystem's MicroSeq microbialidentification system.

The data integrator module 642 can incorporate this additionalcontextual data to help describe and analyze the PCR results collectedby EpiMonitor. The data integrator module 642 locates the physicalsource of the additional contextual dataset, parses that information,and associates the contextual data with uploaded instance data fromclient systems. To facilitate operation of the data integrator module642, an EpiMonitor interface standard can be made publicly available sothat a publisher of a new data store can design their data storeaccordingly, or write their own private embodiment of a data integratormodule so that EpiMonitor 100 can attain the new data.

While useful results are obtained by populating and analyzing theEpiMonitor database 636, integrating the database with theabove-described laboratory response network (LRN), pictureddiagrammatically at 50, provides additional benefit. To accomplish thisintegration, the LRN 50 includes a component of the EpiMonitor softwareplatform 100, which allows the information stored in the database 636 tobe made available to the LRN 50 and also to integrate with thehierarchical reporting rules defined by the LRN 50. The EpiMonitorsoftware platform 100 includes a set of programmable business rules 652that define how the database 636 integrates with the LRN 50. With theknowledge of the governing rule set of the LRN 50, the EpiMonitorsoftware platform 100 integrates its database 636 into the LRN schema.

The EpiMonitor software platform 100 is designed to be highly efficientin gathering data from a diverse collection of instruments, potentiallylocated across a widely dispersed geographic area. As the results arereported from each instrument, they are stored in the database 636 andlinked with the LRN 50 according to the LRN business rules 652. In thisway, if a suspected target sequence is detected in a statisticallysignificant amount, the LRN 50 receives instant notification. The LRN 50can then immediately forward an alert to other networks, such as amilitary network, and also to other systems connected through theEpiMonitor software platform 100. The response to a positive detectionof statistical significance can be to send messages, possibly throughthe EpiMonitor software platform, to instruments at other geographiclocations to begin testing for the target sequence as well.

Integration is not limited only to the LRN 50 (in its present form orfuture forms). Rather, the EpiMonitor software platform 100 is flexibleand will allow integration of database 636 into any information system.If that information system employs predefined rules, the EpiMonitorsoftware platform 100 can be configured to embed those rules in itsbusiness rules database 652.

A general framework for acquiring knowledge is useful as thedistribution of instruments 176, data types (originating from real-timePCR, sequencing, etc.), end-users (epidemiologists, clinicians),geographical and temporal points/places of interest, target sequences,data distribution, etc. may be constantly changing. EpiMonitor 100 canprovide a framework for data analysts to codify automated ways ofcreating knowledge most important to them and theirdistributions-of-interest.

Specific examples include an analyst in China who may want to “weight”“high” findings captured in Shanghai greater than those from New York.Another end-user may want to assign greater “weight” to data from assaysthat target a certain target sequence. To a clinician, the“co-variation” of a target sequence X with target sequence Y during themonth of December may be of clinical importance. Additionally, datavalues produced by a more sophisticated detection instrument, such as aportable pMD, may be deemed more “sensitive” than that from a handheldpMD. Further, statistical rules can be envisioned, such as ruling datafrom a small-sampled population as less “important” or “critical” thandata originating from a population that is more highly sampled.

The qualifiers and conclusions shown above in quotation marks areexamples of assertions in the EpiMonitor knowledge framework that givequalitative, abstract meaning to the quantitative data. EpiMonitorprovides a framework for codifying this knowledge and an engine toinstantiate this knowledge on the aggregated data and results fed toEpiMonitor. The framework consists of an ontology to structure the ideasof the domain (where ideas such as “assays,” “weights,” “thresholdcycle,” “instrument,” “environment,” and “sample” are defined in acomputable environment). A rules engine/interface allows users to codetheir assertions and knowledge about the domain using ideas in theontology, and an inference engine applies the rules to the data toproduce knowledge.

In addition, statements, rules, or sentences can be proposed byEpiMonitor software platform 100 itself after learning how users shapeand codify their own rules. For example, if a user asserts a statementin the knowledge base such as “a covariation of target sequence X and Yleads to a clinical implication of Z,” then EpiMonitor software platform100 can search the space of all X and Y for other clinical implicationsof Z. Another example, if a user asserts that “Influenza A and HumanAdenovirus vary in distribution in a co-variate manner,” EpiMonitorsoftware platform 100 could suggest rules for other pairs of targetsequences that appear to co-vary together given the aggregated results.

Further examples of the operation of the knowledge framework within theEpiMonitor software platform 100 are as follows. EpiMonitor softwareplatform 100 can use statistics to judge whether or not new data isvalid. A simple illustration is that if only one data point iscollected, the confidence of the measured statistic is very low; as moremeasurements are collected, the confidence estimate may go up, and thesoftware can tag the outcome appropriately (such as by attachingconfidences that sampled results are not false positives or falsenegatives). The software can be adapted to use other statistical/machinelearning techniques for anomaly detection as well, including thosedeveloped by the Centers for Disease Control (CDC).

Weighting techniques can also take into account spatial and temporalinformation. For example, if the flu intensifies cyclically everyDecember, for example, those findings of flu that occur in the middle ofthe cycle can be weighted lower than findings from other seasons becausethe in-season incidences are expected. Higher flu detection ratesoutside of the expected cycle may point to a pending outbreak. Anexample of spatial processing is when an event with international draw,such as a soccer tournament, is hosted in a city. The influx of peoplefrom diverse locales may cause a target sequence to be detected that isnot commonly found in the city. Spatial recognition might weight thisfinding lower because it has a known source. The data is not discarded,however, and can be used in later analysis. Data can further beprocessed using quality-control metrics. Data collected acrosslaboratories and instruments may be of different quality. For example, asample may be PCR-inhibitory, the instrument may not be calibratedcorrectly, etc. A quality score reflecting these deficiencies can beused to appropriately weight or normalize data.

Further, EpiMonitor software platform 100 provides a framework to minedata such as, for example to apply probabilities and classify trends.Statistical and machine pattern recognition techniques can accept asinput the current body of quantitative and qualitative results and thecontextual variables collected along with it. The pattern recognitiontechniques then classify and assign Bayesian-type probabilities to newdata given the present corpus of data. A real-life exercise involvingprobabilities includes gauging a result in a particular context. Forexample: “What is the probability of a ‘positive response’ given thatthe real-time PCR result=X and the sample collection location is ‘SanFrancisco, Calif.,’ the temperature when the sample was collected was 60degrees, the instrument was an ABI 7900, the assay performancecharacteristics are X, Y, and Z, and the period of collection isDecember-January?”

In various embodiments, EpiMonitor software 100 can identify trends inprevious outbreaks, identify trends in current data, and compare currenttrends with previous trends to recognize possible outbreaks. Real-lifeexamples of data mining include using temporal analysis techniques toenable the classification of the next outbreak given the prior temporaldata of outbreaks, or classifying where influenza may spread given theprior spatial distribution of an influenza epidemic. The sensitivity andspecificity of predictive measures improve, as a function of data andtime, as the distribution of data acquired by EpiMonitor software 100more closely resembles the actual population. Further, as temporal andspatial data points increase, the EpiMonitor software 100 will be ableto better predict yearly, seasonal, migratory, and regional trends.

Data mining analysis can also evaluate the effectiveness ofcountermeasures, such as travel restrictions, prescribed drugs, etc.,when applied to an outbreak scenario. Much of the information regardingcountermeasures can be found in the public domain: for example, travelrestrictions and medication sales (such as the National Retail DataMonitor). Another source of information to make data mining moreaccurate and powerful can be the inclusion of syndromic data (such aschief complaints by patients, lab results, a clinician's findings)captured by syndromic surveillance systems, such as RODS (Real-timeOutbreak and Disease Surveillance). For example, a positive result forthe Influenza A assay in EpiMonitor software 100 can be bolstered by thepatient's chief complaint of Influenza A-caused syndromes or by thepopulation's collective syndromic trends presented by these othersystems.

In various embodiments, the EpiMonitor software platform 100 supportslearning rules to apply to new data. For example, a ruleset that isassigned to a particular assay X, instrument Y, or sample Z, can belabeled XYZ. The aggregated data pertaining to XYZ may have a certaindistribution, such as Gaussian, with parameters such as mean andvariance. In this way, algorithms that are abstract to distributions canbe used, such as support vector machines. Algorithms can be specific fordistributions, such as expectation-maximization, which uses a labeleddata set for a particular sample as a training set to learn how to labelan unlabeled set. Such algorithms may also suggest alternative labelsfor distributions already labeled. More simply, a maximum-likelihoodapproach may be taken, whereby a probability is estimated based ondistributions of the existing data set. In various embodiments,neural-network type classifiers can also be implemented.

In various embodiments, the EpiMonitor software platform 100 candetermine cyclical patterns of disease migration through time. Givenreal-time PCR qualitative data over a period of time (e.g., the numberof PLUS Bordetella pertussis results in a given period), an outbreak canbe predicted. Relevant knowledge includes how copy counts of Bordetellapertussis are associated with illness (i.e., did a PLUS count ofBordetella pertussis really cause respiratory disease X?). Seasonaleffects are also helpful; for example, whether copy counts of Bordetellapertussis may be generally higher during the month of January. Withoutprior knowledge of an outbreak's distribution, time domain signalprocessing techniques can be used, such as discussed below. In variousembodiments, the EpiMonitor software platform 100 can be populated withhistoric data of disease outbreaks, which may or may not include diseasedata.

Methodologies presented here can operate on numerical data (such asthreshold cycles or gene copy numbers) and/or clinical data. In additionto time-domain processes, information can be transformed into thefrequency domain using common tools such as the Fourier transform. Then,the frequency content can be filtered according to the needs of theEpiMonitor platform 100 and the data transformed back to the timedomain. A selection of time domain techniques that can be employedincludes CUSUM (cumulative sum), Generalized Linear Model, ExponentialWeighted Moving Average, and ARIMA (Auto-Regressive Integrated MovingAverage).

CUSUM, implemented in the CDC's Early Aberration Reporting System(EARS), involves the following calculation:

${S\_ t} = {\max {\left\{ {0,{{S\_ t} - 1 + \frac{{X\_ t} - \left( {{mean\_ t} + {k*{s\_ t}}} \right)}{s\_ t}}} \right\}.}}$

S_t is the CUSUM calculation on day t, X_t is the signal on day t,mean_t and s_t are the mean and standard deviation, respectively, of thebaseline signal reading (a period, possibly a week, in which no outbreakhas occurred), and k is the shift from the mean to be detected.

The Generalized Linear Model, implemented in the CDC's BioSense program(as part of the Public Health Information Network) attempts to take intoaccount day-of-week and other temporal, such as seasonal, factors. Itcan be calculated as follows: E(X_t)=B_(—)0+B_(—)1(Sunday)+ . . .+B_(—)6(Friday)+B_(—)7(January)+ . . . +B_(—)17(November)+ . . .+B_(—)19(Holiday)+ . . . +B_(—)19(time trend). The expected counts onday t are defined using a generalized linear model with a particulardistribution. The test statistic is the probability of observing atleast X_t cases given E(X_t).

The Exponential Weighted Moving Average, implemented in the Departmentof Defense's ESSENCE (Electronic Surveillance System for the EarlyNotification of Community-based Epidemics) system, can be calculatedusing the equation Y_t=omega*X_t+(1−omega)*Y_t−1, where Y_(—)1=X_(—)1.The test statistic is (Y_t−mean_t)/(s_t*[omega/(2-omega)]̂0.5). Y_t isthe smoothed daily value for some smoothing parameter omega, and X_t,s_t, and mean_t are defined in the same manner as for the CUSUM method.

ARIMA (Auto-Regressive Integrated Moving Average). Auto regression is alinear regression of the current value of a series against one or moreprior values of the series. A moving average can be calculated as shownabove for the Exponential Weighted Moving Average. ARIMA combines boththe auto regression and moving average methods, which appears to moreeffectively correct for seasonal effects.

In various embodiments, the EpiMonitor software platform 100 can alsoanalyze disease through location data. Spatial domain techniques includeSaTScan and WSARE (What's Strange About Recent Events). SaTScan softwarehas been developed to analyze spatial, temporal, and space-time countdata using the spatial, temporal, or space-time scan statistics. Inother words, it is used to test spatial clusters of disease outbreaks todistinguish between random and statistically significant data. SaTScanrelies on events being defined, which can include whether a patientcarries a particular syndrome based on their PCR data being over acertain threshold for a particular target sequence. SaTScan can use aPoisson-based model, where the number of events in an area isPoisson-distributed according to a known underlying population at risk,a Bernoulli model with 0/1 event data such as cases and controls, aspace-time permutation model using only case data, an ordinal model forcategorical data, or an exponential model for survival time data with orwithout censored variables.

WSARE searches for uniqueness using a combination of values (co-variate)under a set of rules. For example, a rule could be “Gender=Male and HomeLocation=94404.” This rule determines whether male patients whoselocation (postal code) is 94404 have a particularly high reading forsome target sequence X. WSARE searches among possible rules and selectsthe most statistically significant rule for the current time period.

In various embodiments, the EpiMonitor software platform 100 canassociate disease diagnoses and symptoms to multivariate PCR results.When EpiMonitor software platform 100 is first installed, priorknowledge and archival data will be limited, thus limiting theeffectiveness of the learning methods that rely on a set of learningdata. EpiMonitor software platform 100 can then begin associatingdiagnoses and symptoms to PCR results through correlation. BecauseEpiMonitor software platform 100 provides an infrastructure to collectthis data about disease, symptoms, context, and PCR results, thiscorrelation-type of study may be achieved.

In various embodiments, the EpiMonitor software platform 100 can beemployed to determine the effects of platform and assay on copy count.Aggregating data over many permutations of assay, sample, and platformtypes can yield knowledge of how sensitive/specific a particulardetection combination is within the context of confirmatory results orpatient syndromic information. Statistically significant numbers ofanalyses can also be performed, creating more trustworthy normalizationdata. Other factors that the EpiMonitor software platform 100 cananalyze include the host-susceptibility or host-resistance ofpopulations and regions to a certain pathogen, response of a populationto therapeutics, and mitigation measures. Mitigation measures caninclude travel restrictions, prescribed drugs, vaccines, etc.

The EpiMonitor software platform 100 can be configured into differentlayers. For example, if systems using the EpiMonitor software platform100 exist for different entities, such as the CDC, the Army, Navy,private hospitals, etc., the EpiMonitor software 100 can be readilyconfigured to add another computational layer to those already utilizedby each entity. This higher layer would have the capability to utilizeinformation from lower layers (i.e., systems deployed by the differententities) to analyze data at a higher level of abstraction for an entitysuch as an overseeing federal agency, the World Health Organization(WHO), etc. Because information about assays, targets, and experimentalmethods are stored in EpiMonitor databases, the data can be relatedbetween these distinct sources.

An example application of the EpiMonitor software platform 100 ispresented merely to illustrate some of the possibilities of the softwareplatform. In this example, the Laboratory Research Network (LRN) 50 isin communication with a military network. The military network, throughmethods that do not need to be disclosed to the LRN 50, may detect anincreased probability that a terrorist group intends to release apathogen into the United States at a certain port of entry. Even thoughthe precise nature of the pathogen may not be known, certain parameters(which may be associated with efficacy or transportability) might beassociated with several known viral agents. Based on this information,the LRN 50 and/or the EpiMonitor software platform 100 can determine abattery of genetic assay tests that would be most effective in detectingthe pathogen, should it be introduced.

Using the EpiMonitor software platform 100, the LRN 50 communicates withreader-analyzer instruments 176 in the geographic vicinity of thetargeted port of entry. The reader-analyzer instruments 176 can includedisplays that instruct instrument operators to begin testing using aprescribed assay panel. The assay panels can be kept in storage (such asfreezers) at central distribution points and forwarded as needed. Invarious embodiments, a library of different assay panels may beavailable at the lab or a smaller library may be carried by an operatorof a handheld instrument 56 or portable instrument 54. If ISAP modules172 are provided with communication capability and able to custom-fillmicrofluidics cards, information to assemble suitable assays can be sentdirectly to the ISAP modules 172 from the EpiMonitor software platform100. Because the EpiMonitor software platform 100 supports peer-to-peercommunication, as well as communication through a central network (e.g.,the Internet), the alert can propagate quickly. Peer-to-peercommunication among instruments provides further assurance that allinstruments receive the alert, even those that are not communicatingdirectly with the LRN.

Once the microfluidics card 174 has been prepared and inserted into thereader-analyzer instrument 176, the reader-analyzer instruments 176 willcollect data, typically by optical analysis of fluorescence signals, todetermine if the target sequences are present. In a real-time PCRsystem, individual data collection steps can occur after each thermalcycle and these individual data sets can be analyzed to producequantitative information about the suspected target sequence. As data iscollected concerning individual samples, the LRN 50 can construct anaccurate picture of where certain target sequences are occurring. Thisinformation can be fed back to the military network to improve itsunderstanding of an emerging terrorist incident.

For example, assay panels can be developed that can test for DNA regionsof interest within plants. This is useful to analyze whether DNAintroduced into genetically modified organisms (GMOs) is spreading toother non-GMO crops, or even into indigenous plant species.Additionally, panels can be created that can test for DNA regions ofinterest within bacteria or insects. Such regions of interest can be DNAcorresponding to drug or pesticide resistance. Samples can be obtainedby farmers and/or agricultural workers, and can be processed on-site orsubmitted to a central or regional testing center. In variousembodiments, PCR may be used to analyze samples. The information fromeach sample can be used by EpiMonitor software platform 100 to assessthe spread and prevalence of sequences of interest.

In various embodiments, the present teachings can employ any of avariety of universal detection approaches involving real-time PCR andrelated approaches. For example, the present teachings contemplatevarious embodiments in which an encoding ligation reaction is performedin a first reaction vessel (such as for example, an eppendorf tube), anda plurality of decoding reactions are then performed in microfluidiccard 174 described herein. For example, a multiplexed oligonucleotideligation reaction (OLA) can be performed to query a plurality of targetDNA, wherein each of the resulting reaction products is encoded with,for example, a primer portion, and/or, a universal detection portion. Byincluding a distinct primer pair in each of a plurality of wells ofmicrofluidic card 174 corresponding to the primers sequences encoded inthe OLA, a given encoded target DNA can be amplified by that distinctprimer pair in a given well of plurality of wells. Further, a universaldetection probe (such as, for example, a nuclease cleavable TaqMan®probe) can be included in each of plurality of wells of microfluidiccard 174 to provide for universal detection of a single universaldetection probe.

Such approaches can result in a universal microfluidic card 174 with itsattendant benefits including, among other things, one or more ofeconomies of scale, manufacturing, and/or ease-of-use. The nature of themultiplexed encoding reaction can comprise any of a variety oftechniques, including a multiplexed encoding PCR pre-amplification or amultiplexed encoding OLA. Further, various approaches for encoding afirst sample with a first universal detection probe, and a second samplewith a second universal detection probe, thereby allowing for two samplecomparisons in a single microfluidic card 174, can also be performedaccording to the present teachings. Illustrative embodiments of suchencoding and decoding methods can be found for example in PCTPublication No. WO2003U.S. Pat. No. 0,029,693 to Aydin et al., PCTPublication No. WO2003U.S. Pat. No. 0,029,967 to Andersen et al., U.S.Provisional Application Nos. 60/556,157 and 60/630,681 to Chen et al.,U.S. Provisional Application No. 60/556,224 to Andersen et al., U.S.Provisional Application No. 60/556,162 to Livak et al., and U.S.Provisional Application No. 60/556,163 to Lao et al.

In various embodiments, the detection probes can be suitable fordetecting single nucleotide polymorphisms (SNPs). A specific example ofsuch detection probes comprises a set of four detection probes that areidentical in sequence but for one nucleotide position. Each of the fourdetection probes comprises a different nucleotide (A, G, C, and T/U) atthis position. The detection probes can be labeled with probe labelscapable of producing different detectable signals that aredistinguishable from one another, such as different fluorophores capableof emitting light at different, spectrally resolvable wavelengths (e.g.,4-differently colored fluorophores). In various embodiments, for exampleSNP analysis, two colors can be used for two known variants.

In various embodiments, at least one of the forward primer and thereverse primer can further comprise a detection probe. A detection probe(or its complement) can be situated within the forward primer betweenthe first primer sequence and the sequence complementary to the targetDNA, or within the reverse primer between the second primer sequence andthe sequence complementary to the target DNA. A detection probe cancomprise at least about 10 nucleotides up to about 70 nucleotides and,more particularly, about 15 nucleotides, about 20 nucleotides, about 30nucleotides, about 50 nucleotides, or about 60 nucleotides. In variousembodiments, a detection probe (or its complement) can further comprisea Zip-Code™ sequence (marketed by Applied Biosystems). In variousembodiments, a detection probe can comprise an electrophoretic mobilitymodifier, such as a nucleobase polymer sequence that can increase thesize of a detection probe, or in various embodiments, a non-nucleobasemoiety that increases the frictional coefficient of the detection probe,such as those mobility modifier described in commonly-owned U.S. Pat.Nos. 5,470,705, 5,514,543, 5,580,732, and 5,624,800 to Grossman.

A detection probe comprising a mobility modifier can exhibit a relativemobility in an electrophoretic or chromatographic separation medium thatallows a user to identify and distinguish the detection probe from othermolecules comprised by the sample. In various embodiments, a detectionprobe comprising a sequence complementary to a detection probe and anelectrophoretic mobility modifier can be, for example, a ZipChute™detection probe (marketed by Applied Biosystems). In variousembodiments, hybridization of a detection probe with an amplicon,followed by electrophoretic analysis, can be used to determine theidentity and quantity of the target DNA. In various embodiments, themethods of the present teachings can include forming a detection mixturecomprising a detection probe set ligation sequence, and a primer set.

In various embodiments, any detection probe set ligation sequencecomprised by the detection mixture can be amplified using PCR onreader-analyzer instrument 176 and thereby form an amplificationproduct. In various embodiments, detection of amplification of anydetection probe ligation sequence of an analyte. In various embodiments,detection of amplification by reader-analyzer instrument 176 cancomprise detection of binding of a detection probe to a detection probehybridization sequence comprised by a probe set ligation sequence or anamplification product thereof. In various embodiments, detecting cancomprise contacting a PCR amplification product such as an amplifiedprobe set ligation sequence with a detection probe comprising a labelunder hybridizing conditions.

In various embodiments for amplification of a polynucleotide, assay cancomprise a preamplification product, wherein one or more polynucleotidesin an analyte have been amplified prior to being deposited in at leastone of the plurality of wells. In various embodiments, these methods canfurther comprise forming a plurality of preamplification products bysubjecting an initial analyte comprising a plurality of polynucleotidesto at least one cycle of PCR to form a detection mixture comprising aplurality of preamplification products. The detection mixture ofpreamplification products can be then used for further amplificationusing microfluidic card 174 and reader-analyzer instrument 176. Invarious embodiments, preamplification comprises the use of isothermalmethods.

In various embodiments, a two-step multiplex amplification reaction canbe performed wherein the first step truncates a standard multiplexamplification round to boost a copy number of the DNA target by about100-1000 or more fold. Following the first step, the resulting productcan be divided into optimized secondary single amplification reactions,each containing one or more of the primer sets that were used previouslyin the first or multiplexed booster step. The booster step can occur,for example, using an aqueous target or using a solid phase archivednucleic acid. See, for example, U.S. Pat. No. 6,605,452, Marmaro.

In various embodiments, preamplification methods can employ in vitrotranscription (IVT) comprising amplifying at least one sequence in acollection of nucleic acids sequences. The processes can comprisesynthesizing a nucleic acid by hybridizing a primer complex to thesequence and extending the primer to form a first strand complementaryto the sequence and a second strand complementary to the first strand.The primer complex can comprise a primer complementary to the sequenceand a promoter region in anti-sense orientation with respect to thesequence. Copies of anti-sense RNA can be transcribed off the secondstrand. The promoter region, which can be single or double stranded, canbe capable of inducing transcription from an operably linked DNAsequence in the presence of ribonucleotides and a RNA polymerase undersuitable conditions. Suitable promoter regions may be prokaryoteviruses, such as from T3 or T7 bacteriophage.

In various embodiments, the primer can be a single stranded nucleotideof sufficient length to act as a template for synthesis of extensionproducts under suitable conditions and can be poly(T) or a collection ofdegenerate sequences. In various embodiments, the methods involve theincorporation of an RNA polymerase promoter into selected cDNA moleculeby priming cDNA synthesis with a primer complex comprising a syntheticoligonucleotide containing the promoter. Following synthesis ofdouble-stranded cDNA, a polymerase generally specific for the promotercan be added, and anti-sense RNA can be transcribed from the cDNAtemplate. The progressive synthesis of multiple RNA molecules from asingle cDNA template results in amplified, anti-sense RNA (aRNA) thatserves as starting material for cloning procedures by using randomprimers. The amplification, which will typically be at least about20-40, typically to 50 to 100 or 250-fold, but can be 500 to 1000-foldor more, can be achieved from nanogram quantities or less of cDNA.

In various embodiments, a two stage preamplification method can be usedto preamplify assay in one vessel by IVT and, for example, thispreamplification stage can be 100× sample. In the second stage, thepreamplified product can be divided into aliquots and preamplified byPCR and, for example, this preamplification stage can be 16,000× sampleor more. Although the above preamplification methods can be used inmicrofluidic card 174, these are only examples and are non-limiting.

In various embodiments, the preamplification can be a multiplexpreamplification, wherein the analyte sample can be divided into aplurality of aliquots. Each aliquot can then be subjected topreamplification using a plurality of primer sets for DNA targets. Invarious embodiments, the primer sets in at least some of the pluralityof aliquots differ from the primer sets in the remaining aliquots. Eachresulting preamplification product detection mixture can then bedispersed into at least some of the plurality of wells of microfluidiccard 174 comprising an assay having corresponding primer sets anddetection probes for further amplification and detection according tothe methods described herein. In various embodiments, the primer sets ofassay in each of the plurality of wells can correspond to the primersets used in making the preamplification product detection mixture. Theresulting assay 1000 in each of the plurality of wells 26 thus cancomprise a preamplification product and primer sets and detection probesfor amplification for DNA targets, which, if present in the analytesample, have been preamplified.

Since a plurality of different sequences can be amplified simultaneouslyin a single reaction, the multiplex preamplification can be used in avariety of contexts to effectively increase the concentration orquantity of a sample available for downstream analysis and/or assays. Invarious embodiments, because of the increased concentration or quantityof target DNA, significantly more analyses can be performed withmultiplex amplified samples than can be performed with the originalsample. In various embodiments, multiplex amplification further permitsthe ability to perform analyses that require more sample or a higherconcentration of sample than was originally available. In variousembodiments, multiplex amplification enables downstream analysis forassays that could not have been possible with the original sample due toits limited quantity.

In various embodiments, the plurality of aliquots can comprise 16aliquots with each of the 16 aliquots comprising about 1536 primer sets.In various embodiments, a sample comprising a whole genome for aspecies, for example a human genome, can be preamplified. In variousembodiments, the plurality of aliquots can be greater than 16 aliquots.In various embodiments, the number of primer sets can be greater than1536 primer sets. In various embodiments, the plurality of aliquots canbe less than 16 aliquots and the number of primer sets can be greaterthan 1536 primer sets. For examples of various embodiments, see PCTPublication No. WO 2004/051218 to Andersen and Ruff.

In various embodiments, assay can be preamplified, as discussed herein,in order to increase the amount of target DNA prior to distribution intoa plurality of wells of a microplate. In various embodiments, assay canbe collected, for example, via a needle biopsy that typically yields asmall amount of sample. Distributing this sample across a large numberof wells can result in variances in sample distribution that can affectthe veracity of subsequent gene expression computations. In suchsituations, assay can be preamplified using, for example, a pooledprimer set to increase the number of copies of all target DNAsimultaneously.

In various embodiments, preamplification processes can be non-biased,such that all target DNA are amplified similarly and to about the samepower. In various embodiments, each target DNA can be amplifiedreproducibly from one input sample to the next input sample. Forexample, if target DNA X is initially present in sample A at 100 targetmolecules, then after 10 cycles of PCR amplification (1000-fold),100,000 target molecules should be present. Continuing with the example,if target DNA X is initially present in sample B at 500 targetmolecules, then after 10 cycles of PCR amplification (1000-fold),500,000 target molecules should be present. In this example, the ratioof target DNA X in samples A/B remains constant before and after theamplification procedure.

In various embodiments, a minor proportion of all target DNA can have anobserved preamplification efficiency of less than 100%. In variousembodiments, if the amplification bias is reproducible and consistentfrom one input sample to another, then the ability to accurately computecomparative relative quantitation between any two samples containingdifferent relative amounts of target can be maintained. Continuing theexample from above and assuming 50% reproducible amplificationefficiency, if target DNA X is initially present in sample A at 100target molecules, then after 10 cycles of PCR amplification (50% of1000-fold), 50,000 target molecules should be present. Furthercontinuing the example, if target X is initially present in sample B at500 target molecules, then after 10 cycles of PCR amplification (50% of1000-fold), 250,000 target molecules should be present. In this example,the ratio of template X in samples A/B remains constant before and afterthe amplification procedure and is the same ratio as the 100% efficiencyscenario.

In various embodiments, an unbiased amplification of each target DNA (x,y, z, etc.) can be determined by calculating the difference in CT valueof the target DNA (x, y, z, etc.) from the C_(T) value of a selectedendogenous reference, and such calculation is referred to as the ΔC_(T)value for each given target DNA, as described above. In variousembodiments, a reference for a bias calculation can be non-preamplified,amplified target DNA and an experimental sample can be a preamplifiedamplified target DNA. In various embodiments, the standard sample andexperimental sample can originate from the same sample, for example,same tissue, same individual and/or same species. In variousembodiments, comparison of ΔC_(T) values between the non-preamplifiedamplified target DNA and preamplified amplified target DNA can provide ameasure for the bias of the preamplification process between theendogenous reference and the target DNA (x, y, z, etc.).

In various embodiments, the difference between the two ΔC_(T) values(ΔΔC_(T)) can be zero and as such there is no bias frompreamplification. This is explained in greater detail below withreference to FIG. 20. In various embodiments, the gene expressionanalysis system can be calibrated for potential differences inpreamplification efficiency that can arise from a variety of sources,such as the effects of multiple primer sets in the same reaction. Invarious embodiments, calibration can be performed by computing areference number that reflects preamplification bias. Reference numbersimilarity for a given target DNA across different samples is indicativethat the preamplification reaction ΔC_(T)s can be used to achievereliable gene expression computations.

In various embodiments of the present teaching, a gene expressionanalysis system can compute these reference numbers by collecting asample (designated as Sample A (S_(A))) and processing it with one ormore protocols. A first protocol comprises running individual PCR geneexpression reactions for each target DNA (T_(x)) relative to anendogenous reference (endo), such as, for example, 18s or GAPDH. Thesereactions can yield cycle threshold values for each target DNA relativeto the endogenous control; as computed by:

ΔC _(T not preamplitied) T _(x) S _(A) =C _(T not preamplified) T _(x) S_(A) −C _(T notpreamplitied) endo

A second protocol can comprise running a single PCR preamplificationstep on assay with, for example, a pooled primer set. In variousembodiments, the pooled primer set can contain primers for each targetDNA. Subsequently, the preamplified product can be distributed among aplurality of wells of a microplate. PCR gene-expression reactions can berun for each preamplified target DNA (T_(x)) relative to an endogenousreference (endo). These reactions can yield cycle threshold values foreach preamplified target DNA relative to the endogenous control, ascomputed by:

ΔC _(T preamplified) T _(x) S _(A) =C _(T preamplified) T _(x) S _(A) −C_(T preamplified endo) T _(x) S _(A)

A difference between these ΔC_(T not preamplified) T_(x)S_(A) andΔC_(T preamplified) T_(x)S_(A) can be computed by:

ΔΔC _(T) T _(x) S _(A) =ΔC _(T not preamplified) T _(x) S _(A) −ΔC_(T preamplified) T _(x) S _(A)

In various embodiments, a value for ΔΔC_(T)T_(x)S_(A) can be zero orclose to zero, which can indicate that there is no bias in thepreamplification of target DNA T_(X). In various embodiments, a negativeΔΔC_(T) T_(x)S_(A) value can indicate the preamplification process wasless than 100% efficient for a given target DNA (T_(x)). For example,when using an IVT preamplification process, a percentage of target DNAwith a ΔΔC_(T) of +/−1 C_(T) of zero can be ˜50%. In another example,when using a multiplex preamplification process, a percentage of targetDNA with a ΔΔCT of +/−1 C_(T) of zero can be ˜90%.

In various embodiments, amplification efficiency can be less than 100%for a particular target DNA, therefore ΔΔC_(T) is less than zero for theparticular target DNA. An example can be an evaluation of ΔΔC_(T) valuesfor a group of target DNA from a 1536-plex for the multiplexpreamplification process including four different human sample inputsources: liver, lung, brain and an universal reference tissue composite.In this example, most ΔΔC_(T) values are near zero, however, some of thetarget DNA have a negative ΔΔC_(T) value but these negative values arereproducible from one sample input source to another. In variousembodiments, a gene expression analysis system can determine if a biasexists for target DNA analyzed for different sample inputs. Otherapparatus, compositions, and methods that may be useful herein can befound in commonly assigned U.S. patent application Ser. No. 11/086,261.

Some embodiments and the examples described herein are exemplary and notintended to be limiting in describing the full scope of compositions andmethods of these teachings. Equivalent changes, modifications, andvariations of some embodiments, materials, compositions, and methods canbe made within the scope of the present teachings, with substantiallysimilar results.

1-25. (canceled)
 26. A method of performing genetic surveillance,comprising: using at least one reader-analyzer instrument to performgenetic assay analysis of a sample obtained from an individual member ofa population and to generate genetic surveillance-related data based onsaid analysis; associating spatial information and temporal informationwith said genetic surveillance-related data; and communicating saidspatial information and temporal information associated with saidgenetic surveillance-related data over a network using saidreader-analyzer instrument to effect said communication.
 27. The methodof claim 26 further comprising storing said spatial information andtemporal information with associated said genetic surveillance-relateddata in a database as information reflecting the status of saidpopulation.
 28. The method of claim 26 further comprising communicatingsaid spatial information and temporal information associated with saidgenetic surveillance-related data to a laboratory response network. 29.The method of claim 26 further comprising using at least one portablereader-analyzer instrument to perform genetic assay analysis of saidsample.
 30. The method of claim 26 further comprising communicating saidspatial information and temporal information associated with saidgenetic surveillance-related data from a first reader-analyzerinstrument to a second reader-analyzer instrument.
 31. The method ofclaim 30 wherein said communication from a first reader-analyzerinstrument to a second reader-analyzer instrument is effectedpeer-to-peer.
 32. The method of claim 26 further comprising sendingcontrol instructions to said at least one reader-analyzer instrument viasaid network.
 33. The method of claim 26 further comprising altering themanner in which said genetic surveillance-related data are generatedbased on information received by said reader-analyzer over said network.34. The method of claim 26 further comprising performing samplepreparation steps on said sample based on information received over saidnetwork.
 35. The method of claim 34 wherein said sample preparationsteps include manipulation of at least one assay.
 36. The method ofclaim 34 wherein said sample preparation steps include manipulation ofat least one assay and wherein said assay manipulation is performedbased on information received over said network.
 37. The method of claim26 further comprising generating said surveillance-related data bycollecting data identifying a presence or absence of at least onepathogenic organism.
 38. The method of claim 27 wherein said collectingdata step is performed by analyzing at least one target sequence relatedto the at least one pathogenic organism.
 39. The method of claim 26further comprising collecting demographic information from saidindividual member of a population.
 40. The method of claim 39 whereinsaid demographic information is associated with said spatialinformation, said temporal information and said geneticsurveillance-related data.
 41. The method of claim 26 further comprisingusing said spatial information, said temporal information and saidgenetic surveillance-related data to predict potential danger to thepopulation.
 42. The method of claim 26 further comprising applying alearning algorithm to said spatial information, said temporalinformation and said genetic surveillance-related data to validate dataacquired subsequent to said generation of surveillance-related data. 43.The method of claim 26 wherein said reader-analyzer instrument isassociated with an automatic sensing device and the sample obtainingstep is performed automatically by said automatic sensing device. 44.The method of claim 26 wherein said sample obtaining step is performedby the user of said reader-analyzer instrument. 45-49. (canceled)